AI LAB PROJECT¶

A.A.:2023/2024

In the following study, we are going to analyze four distinct dataset containing information about two different cancerous cells (MCF7 and HCC1806) studied through two different sequencing techniques (SMART and DROP). The goal of the study is to build different predictors capable of classifying each cell as either hypoxic or normoxic looking at its genes given by the sequencing: in order to do so, we are going to build different predictors and analyze their individual performances, so that we can understand which classifier operates with the highest accuracy and whether some predictors perform optimally only in a specific setting.

With this goal in mind, we are going to proceed in the following way: first, we are going to perform an exploratory data analysis on the four datasets, which have been given us both in the original version and in a filtered and normalized version; the analysis will include clustering, dimensionality reduction and sampling of mean and variance of the genes, in order to have a basic understanding of which genes are more relevant and how the datasets have been filtered. Afterwards, we will focus on the study of different supervised learning techniques, trying to identify the best predictor for our scope.

MAP OF THE REPORT¶

  • 0: importing libraries

  • 1: EDA

    • 1.0: importing

    • 1.1: MCF7

      • 1.1.1 READING THE META-DATASET USEFUL SINCE CONTAINS SOME INSIGHTS
      • 1.1.2 OPENING THE MAIN DATASET: UNFILTERED MCF7 DATASET PRODUCED USING THE SMART SEQUENCING TECHNOLOGY
      • 1.1.3 CHECKING MISSING AND NON VALID VALUES IN OUR DATASET
      • 1.1.4 COMPUTATION AND VISUALIZATION OF MEAN, STD, QUANTILES, MAX AND MIN
      • 1.1.5 CHECK OF OUTLIERS
      • 1.1.6 SPARSITY OF DATA
      • 1.1.7 DATASET TRANSPOSITION AND APPLICATION OF STATISTICAL TOOLS
      • 1.1.8 CHECK OF NORMALIZATION OF DATA USING SKEWENESS AND KURTOSIS
      • 1.1.9 DATA TRANSORMATIONS USING LOG 2

    • 1.2 HCC1806

      • 1.2.1 READING THE META-DATASET USEFUL SINCE CONTAINS SOME INSIGHTS
      • 1.2.2 OPENING THE MAIN DATASET: UNFILTERED MCF7 DATASET PRODUCED USING THE SMART SEQUENCING TECHNOLOGY
      • 1.2.3 CHECKING MISSING AND NON VALID VALUES IN OUR DATASET
      • 1.2.4 COMPUTATION AND VISUALIZATION OF MEAN, STD, QUANTILES, MAX AND MIN
      • 1.2.5 CHECK OF OUTLIERS
      • 1.2.6 SPARSITY OF DATA
      • 1.2.7 DATASET TRANSPOSITION AND APPLICATION OF STATISTICAL TOOLS
      • 1.2.8 CHECK OF NORMALIZATION OF DATA USING SKEWENESS AND KURTOSIS
      • 1.2.9 DATA TRANSORMATIONS USING LOG 2

  • 2: Analysis on unfiltered/filtered Smartseq and Unsupervided/Supervised learning on SmartSeq

    • 2.1 MCF7
      • 2.1.0 Importing datasets
      • 2.1.1 Statistical study on mean and variances on cells
        • 2.1.1.1 Study of mean on cells
        • 2.1.1.2 Study of STD on cells
        • 2.1.1.3 DETAILS OF DISTRIBUTION OF MEANS
        • 2.1.1.4 DETAILS OF DISTRIBUTION OF STD
      • 2.1.2 Study on genes by mean and variances on genes
        • 2.1.2.1 Study of mean on cells
        • 2.1.2.2 Study of STD on cells
        • 2.1.2.3 DETAILS OF DISTRIBUTION OF MEANS
        • 2.1.2.4 DETAILS OF DISTRIBUTION OF STD
      • 2.1.3 TRIAL AND ERROR OF PSEUDO FILTERING ON CELLS
      • 2.1.4 TRIAL AND ERROR OF PSEUDO FILTERING ON GENES
      • 2.1.4bis Merging the two pseudo filtering approaches
      • 2.1.5 PCA
      • 2.1.6 Clustering
        • 2.1.6.1 Single linkage
        • 2.1.6.2 Average linkage
        • 2.1.6.3 Complete Linkage
      • 2.1.7 Unsupervised learning applied to MCF7
      • 2.1.8 Looking for most useful and expressed genes across cells
      • 2.1.9 More unsupervised learning
    • 2.2 HCC1806
      • 2.2.0 Importing datasets
      • 2.2.1 Statistical study on mean and variances on cells
        • 2.2.1.1 Study of mean on cells
        • 2.2.1.2 Study of STD on cells
        • 2.2.1.3 DETAILS OF DISTRIBUTION OF MEANS
        • 2.2.1.4 DETAILS OF DISTRIBUTION OF STD
      • 2.2.2 Study on genes by mean and variances on genes
        • 2.2.2.1 Study of mean on cells
        • 2.2.2.2 Study of STD on cells
        • 2.2.2.3 DETAILS OF DISTRIBUTION OF MEANS
        • 2.2.2.4 DETAILS OF DISTRIBUTION OF STD
      • 2.2.3 TRIAL AND ERROR OF PSEUDO FILTERING ON CELLS
      • 2.2.4 TRIAL AND ERROR OF PSEUDO FILTERING ON GENES
      • 2.2.4bis Merging the two pseudo filtering approaches
      • 2.2.5 PCA
      • 2.2.6 Clustering
        • 2.2.6.1 Single linkage
        • 2.2.6.2 Average linkage
        • 2.2.6.3 Complete Linkage
      • 2.2.7 Unsupervised learning applied to MCF7
      • 2.2.8 Looking for most useful and expressed genes across cells
      • 2.2.9 More unsupervised learning

  • 3: Analysis on DropSeq and Unsupervided/Supervised learning on DropSeq

    • 3.1 MCF7
      • 3.1.0 OPENING THE MAIN DATASET: MCF7 DATASET PRODUCED USING THE DROPSEQ TECHNOLOGY
      • 3.1.1 SPARSITY OF THE DATA
      • 3.1.2 DATASET TRANSPOSITION AND APPLICATION OF STATISTICAL TOOLS
      • 3.1.3 LOOKING FOR MOST USEFUL AND EXPRESSED GENES ACROSS CELLS
      • 3.1.4 UNSUPERVISD LEARNING APPLIED TO MCF7
      • 3.1.5 More unsupervised learning
    • 3.2 HCC1806
      • 3.2.0 OPENING THE MAIN DATASET: HCC1806 DATASET PRODUCED USING THE DROPSEQ TECHNOLOGY
      • 3.2.1 SPARSITY OF THE DATA
      • 3.2.2 DATASET TRANSPOSITION AND APPLICATION OF STATISTICAL TOOLS
      • 3.2.3 LOOKING FOR MOST USEFUL AND EXPRESSED GENES ACROSS CELLS
      • 3.2.4 UNSUPERVISD LEARNING APPLIED TO HCC1806
      • 3.2.5 More unsupervised learning

  • 4: Classifiers

    • 4.0 Definitions

      • 4.0.1 Definition of Neural Networks
        • 4.0.1.1 Definition of the neural network on single dataset (dropseq)
        • 4.0.1.2 Definition on merged dropseq
        • 4.0.1.3 Definition on general (all 4 dataset merged)
      • 4.0.2 Definition of KNN
      • 4.0.3 Definition Logistic Regression
      • 4.0.4 Definition of CatBoost

    • 4.1 PREDICTORS SMARTSEQ MCF7

      • 4.1.1 SVM
      • 4.1.2 Random Forest
      • 4.1.3 XGBoost
      • 4.1.4 KNN
      • 4.1.5 Logistic Regression
      • 4.1.6 CatBoost

    • 4.2 PREDICTORS SMARTSEQ HCC1806

      • 4.2.1 SVM
      • 4.2.2 Random Forest
      • 4.2.3 XGBoost
      • 4.2.4 KNN
      • 4.2.5 Logistic Regression
      • 4.2.6 CatBoost

    • 4.3 PREDICTORS DROPSEQ MCF7

      • 4.3.1 SVM
      • 4.3.2 Random Forest
      • 4.3.3 XGBoost and ADABoost
      • 4.3.4 KNN
      • 4.3.5 Logistic Regression
      • 4.3.6 CatBoost

    • 4.4 PREDICTORS DROPSEQ HCC1806

      • 4.4.1 SVM
      • 4.4.2 Random Forest
      • 4.4.3 XGBoost
      • 4.4.4 KNN
      • 4.4.5 Logistic Regression
      • 4.4.6 CatBoost

    • 4.5 PREDICTORS ON DROPSEQ MERGED (Neural Network)

    • 4.6 PREDICTORS ON ALL DATASET MERGED

      • 4.6.1 SVM
      • 4.6.2 Random Forest
      • 4.6.3 XGBoost
      • 4.6.4 KNN
      • 4.6.5 Logistic Regression
      • 4.6.6 CatBoost

  • 5: Voting Classifiers

    • 5.1 SVM
    • 5.2 XGB
    • 5.3 KNN
    • 5.4 Logistic Regression
    • 5.5 CatBoost

  • 6: Predictions

    • 6.1 Prediction on SmartSeq MCF7
      • 6.1.0 Loading dataset Start by opening the dataset "MCF7_SmartS_Filtered_Normalised_3000_Data_test_anonim"
      • 6.1.1 Random Forest
      • 6.1.2 Logistic Regression
      • 6.1.3 SVM
      • 6.1.4 Final considerations
    • 6.2 Prediction on SmartSeq HCC1806
      • 6.2.1 SVM
      • 6.2.2 CatBoost
      • 6.2.3 Logistic Regression
      • 6.2.4 Final considerations
    • 6.3 Prediction on DropSeq MCF7
      • 6.3.0 Start by opening the dataset "MCF7_Filtered_Normalised_3000_Data_test_anonim"
      • 6.3.1 NN
      • 6.3.2 CatBoost
      • 6.3.3 Logistic Regression
      • 6.3.4 Final considerations
    • 6.4 Prediction on DropSeq HCC1806
      • 6.4.0 Start by opening the dataset "HCC1806_Filtered_Normalised_3000_Data_test_anonim"
      • 6.4.1 NN
      • 6.4.2 SVM
      • 6.4.3 Logistic Regression
      • 6.4.4 Final conclusions

¶

  1. importing libraries
In [ ]:
import sys
import sklearn
import pandas as pd
import numpy as np
from numpy import count_nonzero
import seaborn as sns
from scipy import sparse
from scipy.sparse import lil_matrix

import re

from scipy.cluster.hierarchy import dendrogram

#%matplotlib inline     
sns.set(color_codes=True)

import os
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

%matplotlib inline

import matplotlib.pyplot as plt
import seaborn as sns

from sklearn import metrics
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans , AgglomerativeClustering, DBSCAN
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.model_selection import cross_val_score,StratifiedKFold, train_test_split,LeaveOneOut, GridSearchCV, KFold
from sklearn.svm import SVC, NuSVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, f1_score

import xgboost
from xgboost import XGBClassifier

import catboost
from catboost import CatBoostClassifier, Pool, cv

from scipy.cluster.hierarchy import dendrogram, linkage

from sklearn.neural_network import MLPClassifier

¶

  1. Exploratory Data Analysis

¶

1.0) Importing

In [ ]:
mcf_smartseq=pd.read_csv("SmartSeq\MCF7_SmartS_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ", engine='python',index_col=0)
mcf_dropseq=pd.read_csv("DropSeq\MCF7_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ", engine='python',index_col=0)
hcc_smartseq=pd.read_csv("SmartSeq\HCC1806_SmartS_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ", engine='python',index_col=0)
hcc_dropseq=pd.read_csv("DropSeq\HCC1806_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ", engine='python',index_col=0)
In [ ]:
df_MCF7_SMART=mcf_smartseq.T
df_MCF7_DROP=mcf_dropseq.T
df_HCC1806_SMART=hcc_smartseq.T
df_HCC1806_DROP=hcc_dropseq.T
In [ ]:
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler


def prepare_data(df):
    X = df.drop('TARGET', axis=1)
    y = df['TARGET']
    # train test samples
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    # scaler applying 
    scaler = StandardScaler()
    X_train_scaled = scaler.fit_transform(X_train)
    X_test_scaled = scaler.transform(X_test)
    return X_train_scaled, X_test_scaled, y_train, y_test,scaler
In [ ]:
df_MCF7_SMART.loc[df_MCF7_SMART.index.str.contains('Norm'), 'TARGET'] = 0
df_MCF7_SMART.loc[df_MCF7_SMART.index.str.contains('Hypo'), 'TARGET'] = 1

df_MCF7_DROP.loc[df_MCF7_DROP.index.str.contains('Norm'), 'TARGET'] = 0
df_MCF7_DROP.loc[df_MCF7_DROP.index.str.contains('Hypo'), 'TARGET'] = 1

df_HCC1806_SMART.loc[df_HCC1806_SMART.index.str.contains('Norm'), 'TARGET'] = 0
df_HCC1806_SMART.loc[df_HCC1806_SMART.index.str.contains('Hypo'), 'TARGET'] = 1

df_HCC1806_DROP.loc[df_HCC1806_DROP.index.str.contains('Norm'), 'TARGET'] = 0
df_HCC1806_DROP.loc[df_HCC1806_DROP.index.str.contains('Hypo'), 'TARGET'] = 1
In [ ]:
X_train_HCC1806_DROP, X_test_HCC1806_DROP, y_train_HCC1806_DROP, y_test_HCC1806_DROP = prepare_data(df_HCC1806_DROP)
X_train_HCC1806_SMART, X_test_HCC1806_SMART, y_train_HCC1806_SMART, y_test_HCC1806_SMART = prepare_data(df_HCC1806_SMART)
X_train_MCF7_DROP, X_test_MCF7_DROP, y_train_MCF7_DROP, y_test_MCF7_DROP = prepare_data(df_MCF7_DROP)
X_train_MCF7_SMART, X_test_MCF7_SMART, y_train_MCF7_SMART, y_test_MCF7_SMART = prepare_data(df_MCF7_SMART)
In [ ]:
df_basic_HCC_SMART=hcc_smartseq
df_trans_HCC_SMART=df_basic_HCC_SMART.T
df_trans_HCC_SMART['TARGET'] = df_trans_HCC_SMART.columns.to_series().apply(lambda x: 1 if 'Hypo' in x else 0)
df_trans_HCC_SMART.loc[df_trans_HCC_SMART.index.str.contains('Norm'), 'TARGET'] = 0
df_trans_HCC_SMART.loc[df_trans_HCC_SMART.index.str.contains('Hypo'), 'TARGET'] = 1
In [ ]:
X_train_HCC_SMART, X_test_HCC_SMART, y_train_HCC_SMART, y_test_HCC_SMART,scaler_HCC_SMART = prepare_data(df_trans_HCC_SMART)
In [ ]:
df_basic_MCF_SMART=mcf_smartseq
df_trans_MCF_SMART=df_basic_MCF_SMART.T
df_trans_MCF_SMART['TARGET'] = df_trans_MCF_SMART.columns.to_series().apply(lambda x: 1 if 'Hypo' in x else 0)
df_trans_MCF_SMART.loc[df_trans_MCF_SMART.index.str.contains('Norm'), 'TARGET'] = 0
df_trans_MCF_SMART.loc[df_trans_MCF_SMART.index.str.contains('Hypo'), 'TARGET'] = 1
In [ ]:
X_train_MCF_SMART, X_test_MCF_SMART, y_train_MCF_SMART, y_test_MCF_SMART,scaler_MCF_SMART = prepare_data(df_trans_MCF_SMART)
In [ ]:
df_basic_hcc_drop=hcc_dropseq
df_trans_hcc_drop=df_basic_hcc_drop.T
df_trans_hcc_drop['TARGET'] = df_trans_hcc_drop.columns.to_series().apply(lambda x: 1 if 'Hypo' in x else 0)
df_trans_hcc_drop.loc[df_trans_hcc_drop.index.str.contains('Norm'), 'TARGET'] = 0
df_trans_hcc_drop.loc[df_trans_hcc_drop.index.str.contains('Hypo'), 'TARGET'] = 1
In [ ]:
X_train_hcc_drop, X_test_hcc_drop, y_train_hcc_drop, y_test_hcc_drop,scaler_hcc_drop = prepare_data(df_trans_hcc_drop)
In [ ]:
df_basic_MCF7_DROP=mcf_dropseq
df_trans_MCF7_DROP=df_basic_MCF7_DROP.T
df_trans_MCF7_DROP['TARGET'] = df_trans_MCF7_DROP.columns.to_series().apply(lambda x: 1 if 'Hypo' in x else 0)
df_trans_MCF7_DROP.loc[df_trans_MCF7_DROP.index.str.contains('Norm'), 'TARGET'] = 0
df_trans_MCF7_DROP.loc[df_trans_MCF7_DROP.index.str.contains('Hypo'), 'TARGET'] = 1
In [ ]:
X_train_MCF7_DROP, X_test_MCF7_DROP, y_train_MCF7_DROP, y_test_MCF7_DROP,scaler_MCF7_DROP = prepare_data(df_trans_MCF7_DROP)

¶

1.1) MCF7

¶

1.1.1) READING THE META-DATASET USEFUL SINCE CONTAINS SOME INSIGHTS

Firstly we download and open the meta_data file of MCF7 since it provides important information about how the project was conducted.

In [ ]:
df_MCF7_unf_meta = pd.read_csv("SmartSeq/MCF7_SmartS_MetaData.tsv",delimiter="\t",engine='python',index_col=0)
print("Dataframe dimensions:", np.shape(df_MCF7_unf_meta))
print("First column: ", df_MCF7_unf_meta.iloc[ : , 0])

Dataframe dimensions: (383, 8)
First column:  Filename
output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam    MCF7
output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam    MCF7
output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam    MCF7
output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam      MCF7
output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam      MCF7
                                                            ... 
output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam    MCF7
output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam    MCF7
output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam    MCF7
output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam    MCF7
output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam    MCF7
Name: Cell Line, Length: 383, dtype: object
In [ ]:
df_MCF7_unf_meta.shape
Out[ ]:
(383, 8)

As we can see the file has a shape of 383 rows and 8 columns and is made in the following way: the rows represent the cells while the columns give us information about the experiment, e.g. we have the cell line, the plate, the position of the cell, the condition (hypoxia or Normoxia), and other informations as the comment or tag.

Moreover the 383 cells tell us that in the main dataset we will have 383 cells (in that case as columns)

In [ ]:
df_MCF7_unf_meta.head(243)
Out[ ]:
Cell Line Lane Pos Condition Hours Cell name PreprocessingTag ProcessingComments
Filename
output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam MCF7 output.STAR.1 A10 Hypo 72 S28 Aligned.sortedByCoord.out.bam STAR,FeatureCounts
output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam MCF7 output.STAR.1 A11 Hypo 72 S29 Aligned.sortedByCoord.out.bam STAR,FeatureCounts
output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam MCF7 output.STAR.1 A12 Hypo 72 S30 Aligned.sortedByCoord.out.bam STAR,FeatureCounts
output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam MCF7 output.STAR.1 A1 Norm 72 S1 Aligned.sortedByCoord.out.bam STAR,FeatureCounts
output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam MCF7 output.STAR.1 A2 Norm 72 S2 Aligned.sortedByCoord.out.bam STAR,FeatureCounts
... ... ... ... ... ... ... ... ...
output.STAR.3_D8_Hypo_S182_Aligned.sortedByCoord.out.bam MCF7 output.STAR.3 D8 Hypo 72 S182 Aligned.sortedByCoord.out.bam STAR,FeatureCounts
output.STAR.3_D9_Hypo_S183_Aligned.sortedByCoord.out.bam MCF7 output.STAR.3 D9 Hypo 72 S183 Aligned.sortedByCoord.out.bam STAR,FeatureCounts
output.STAR.3_E10_Hypo_S232_Aligned.sortedByCoord.out.bam MCF7 output.STAR.3 E10 Hypo 72 S232 Aligned.sortedByCoord.out.bam STAR,FeatureCounts
output.STAR.3_E11_Hypo_S233_Aligned.sortedByCoord.out.bam MCF7 output.STAR.3 E11 Hypo 72 S233 Aligned.sortedByCoord.out.bam STAR,FeatureCounts
output.STAR.3_E12_Hypo_S234_Aligned.sortedByCoord.out.bam MCF7 output.STAR.3 E12 Hypo 72 S234 Aligned.sortedByCoord.out.bam STAR,FeatureCounts

243 rows × 8 columns

¶

1.1.2) OPENING THE MAIN DATASET: UNFILTERED MCF7 DATASET PRODUCED USING THE SMART SEQUENCING TECHNOLOGY

We start now opening the MCF7 SmartSeq unfiltered dataset since we are interested in analyzing it.

In [ ]:
# The dataset unfiltered is one where we can apply statistical tools to extract valuable insights
df_MCF7_unf = pd.read_csv("SmartSeq/MCF7_SmartS_Unfiltered_Data.txt",delimiter="\ ",engine='python',index_col=0)
print("Dataframe dimensions:", np.shape(df_MCF7_unf))
print("First column: ", df_MCF7_unf.iloc[ : , :])

Dataframe dimensions: (22934, 383)
First column:               "output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                     0            
"MIR6859-1"                                                  0            
"WASH9P"                                                     1            
"OR4F29"                                                     0            
"MTND1P23"                                                   0            
...                                                        ...            
"MT-TE"                                                      4            
"MT-CYB"                                                   270            
"MT-TT"                                                      0            
"MT-TP"                                                      5            
"MAFIP"                                                      8            

             "output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                     0            
"MIR6859-1"                                                  0            
"WASH9P"                                                     0            
"OR4F29"                                                     0            
"MTND1P23"                                                   0            
...                                                        ...            
"MT-TE"                                                      0            
"MT-CYB"                                                     1            
"MT-TT"                                                      0            
"MT-TP"                                                      0            
"MAFIP"                                                      0            

             "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                     0            
"MIR6859-1"                                                  0            
"WASH9P"                                                     0            
"OR4F29"                                                     0            
"MTND1P23"                                                   0            
...                                                        ...            
"MT-TE"                                                      0            
"MT-CYB"                                                    76            
"MT-TT"                                                      0            
"MT-TP"                                                      0            
"MAFIP"                                                      0            

             "output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                     0          
"MIR6859-1"                                                  0          
"WASH9P"                                                     0          
"OR4F29"                                                     0          
"MTND1P23"                                                   0          
...                                                        ...          
"MT-TE"                                                      0          
"MT-CYB"                                                    66          
"MT-TT"                                                      0          
"MT-TP"                                                      1          
"MAFIP"                                                      0          

             "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                     0          
"MIR6859-1"                                                  0          
"WASH9P"                                                     0          
"OR4F29"                                                     0          
"MTND1P23"                                                   0          
...                                                        ...          
"MT-TE"                                                      3          
"MT-CYB"                                                   727          
"MT-TT"                                                      0          
"MT-TP"                                                      0          
"MAFIP"                                                      0          

             "output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                     0          
"MIR6859-1"                                                  0          
"WASH9P"                                                     1          
"OR4F29"                                                     0          
"MTND1P23"                                                   0          
...                                                        ...          
"MT-TE"                                                      3          
"MT-CYB"                                                  2717          
"MT-TT"                                                      1          
"MT-TP"                                                      1          
"MAFIP"                                                      0          

             "output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                     0          
"MIR6859-1"                                                  0          
"WASH9P"                                                    10          
"OR4F29"                                                     0          
"MTND1P23"                                                   0          
...                                                        ...          
"MT-TE"                                                      0          
"MT-CYB"                                                  9326          
"MT-TT"                                                      1          
"MT-TP"                                                      1          
"MAFIP"                                                      0          

             "output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                     0          
"MIR6859-1"                                                  0          
"WASH9P"                                                     1          
"OR4F29"                                                     0          
"MTND1P23"                                                   0          
...                                                        ...          
"MT-TE"                                                      0          
"MT-CYB"                                                  3253          
"MT-TT"                                                      1          
"MT-TP"                                                      4          
"MAFIP"                                                      1          

             "output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                     0          
"MIR6859-1"                                                  0          
"WASH9P"                                                     0          
"OR4F29"                                                     0          
"MTND1P23"                                                   0          
...                                                        ...          
"MT-TE"                                                     14          
"MT-CYB"                                                  7949          
"MT-TT"                                                      4          
"MT-TP"                                                      2          
"MAFIP"                                                      0          

             "output.STAR.1_A7_Hypo_S25_Aligned.sortedByCoord.out.bam"  ...  \
"WASH7P"                                                     0          ...   
"MIR6859-1"                                                  0          ...   
"WASH9P"                                                     0          ...   
"OR4F29"                                                     0          ...   
"MTND1P23"                                                   0          ...   
...                                                        ...          ...   
"MT-TE"                                                      1          ...   
"MT-CYB"                                                    30          ...   
"MT-TT"                                                      0          ...   
"MT-TP"                                                      0          ...   
"MAFIP"                                                      0          ...   

             "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                     0             
"MIR6859-1"                                                  0             
"WASH9P"                                                     1             
"OR4F29"                                                     0             
"MTND1P23"                                                   0             
...                                                        ...             
"MT-TE"                                                      0             
"MT-CYB"                                                   239             
"MT-TT"                                                      0             
"MT-TP"                                                      0             
"MAFIP"                                                      0             

             "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                     0            
"MIR6859-1"                                                  0            
"WASH9P"                                                     1            
"OR4F29"                                                     0            
"MTND1P23"                                                   0            
...                                                        ...            
"MT-TE"                                                      4            
"MT-CYB"                                                  3795            
"MT-TT"                                                      7            
"MT-TP"                                                     14            
"MAFIP"                                                      0            

             "output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                     0            
"MIR6859-1"                                                  0            
"WASH9P"                                                     0            
"OR4F29"                                                     0            
"MTND1P23"                                                   1            
...                                                        ...            
"MT-TE"                                                     12            
"MT-CYB"                                                 12761            
"MT-TT"                                                      4            
"MT-TP"                                                     56            
"MAFIP"                                                      0            

             "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                     1            
"MIR6859-1"                                                  0            
"WASH9P"                                                     0            
"OR4F29"                                                     0            
"MTND1P23"                                                   0            
...                                                        ...            
"MT-TE"                                                      4            
"MT-CYB"                                                  2263            
"MT-TT"                                                      2            
"MT-TP"                                                     11            
"MAFIP"                                                      2            

             "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                     0            
"MIR6859-1"                                                  0            
"WASH9P"                                                     0            
"OR4F29"                                                     0            
"MTND1P23"                                                   0            
...                                                        ...            
"MT-TE"                                                      0            
"MT-CYB"                                                  1368            
"MT-TT"                                                      0            
"MT-TP"                                                      2            
"MAFIP"                                                      1            

             "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                     0            
"MIR6859-1"                                                  0            
"WASH9P"                                                     0            
"OR4F29"                                                     0            
"MTND1P23"                                                   0            
...                                                        ...            
"MT-TE"                                                      1            
"MT-CYB"                                                   570            
"MT-TT"                                                      0            
"MT-TP"                                                      0            
"MAFIP"                                                      0            

             "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                     0            
"MIR6859-1"                                                  0            
"WASH9P"                                                     1            
"OR4F29"                                                     0            
"MTND1P23"                                                   0            
...                                                        ...            
"MT-TE"                                                      6            
"MT-CYB"                                                  3477            
"MT-TT"                                                      3            
"MT-TP"                                                      7            
"MAFIP"                                                      6            

             "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                     1            
"MIR6859-1"                                                  0            
"WASH9P"                                                     1            
"OR4F29"                                                     0            
"MTND1P23"                                                   0            
...                                                        ...            
"MT-TE"                                                      0            
"MT-CYB"                                                   349            
"MT-TT"                                                      0            
"MT-TP"                                                      2            
"MAFIP"                                                      0            

             "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                     0            
"MIR6859-1"                                                  0            
"WASH9P"                                                     4            
"OR4F29"                                                     1            
"MTND1P23"                                                   0            
...                                                        ...            
"MT-TE"                                                      7            
"MT-CYB"                                                  2184            
"MT-TT"                                                      2            
"MT-TP"                                                     28            
"MAFIP"                                                      1            

             "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam"  
"WASH7P"                                                     1           
"MIR6859-1"                                                  0           
"WASH9P"                                                     5           
"OR4F29"                                                     0           
"MTND1P23"                                                   0           
...                                                        ...           
"MT-TE"                                                      4           
"MT-CYB"                                                  1149           
"MT-TT"                                                      2           
"MT-TP"                                                     11           
"MAFIP"                                                      4           

[22934 rows x 383 columns]
In [ ]:
df_MCF7_unf.head()
Out[ ]:
"output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" "output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" "output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" "output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam" "output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam" "output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam" "output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam" "output.STAR.1_A7_Hypo_S25_Aligned.sortedByCoord.out.bam" ... "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" "output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam" "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam"
"WASH7P" 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 1 0 0 0 1 0 1
"MIR6859-1" 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
"WASH9P" 1 0 0 0 0 1 10 1 0 0 ... 1 1 0 0 0 0 1 1 4 5
"OR4F29" 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 1 0
"MTND1P23" 0 0 0 0 0 0 0 0 0 0 ... 0 0 1 0 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
"CPLANE2" 0 0 0 0 4 0 0 0 0 0 ... 5 0 0 0 0 0 0 0 0 0
"MT1XP1" 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
"FBXO42" 159 0 491 0 10 5 120 1 106 9 ... 58 0 0 23 19 78 7 683 389 286
"SZRD1" 65 0 47 0 29 212 175 217 219 2 ... 28 39 4 69 74 45 196 101 98 180
"SPATA21" 12 0 0 0 2 36 46 52 63 0 ... 12 9 0 26 18 6 75 21 30 54

243 rows × 383 columns

In [ ]:
df_MCF7_unf.shape # shape of our unfiltered MCF7 SmartSeq dataset
Out[ ]:
(22934, 383)

The MCF7 SmartSeq unfiltered dataset is made of 22934 rows representing specific genes and 383 columns representing the cells (already available in the meta dataset). Therefore each entry tells us how much of every gene is expressed in every cells. Moreover, each cell has in its name written the state in which it was under (normoxia / hypoxia), and such information was given as a separate column in the meta dataset.

In [ ]:
#We return the indexes of the dataframe, which are the gene symbols
gene_symbls_MCF7 = df_MCF7_unf.index
print("Dataframe indexes: ", gene_symbls_MCF7)

Dataframe indexes:  Index(['"WASH7P"', '"MIR6859-1"', '"WASH9P"', '"OR4F29"', '"MTND1P23"',
       '"MTND2P28"', '"MTCO1P12"', '"MTCO2P12"', '"MTATP8P1"', '"MTATP6P1"',
       ...
       '"MT-TH"', '"MT-TS2"', '"MT-TL2"', '"MT-ND5"', '"MT-ND6"', '"MT-TE"',
       '"MT-CYB"', '"MT-TT"', '"MT-TP"', '"MAFIP"'],
      dtype='object', length=22934)

¶

1.1.3) CHECKING MISSING AND NON VALID VALUES IN OUR DATASET

We now perform some operations to see if there are non numeric or non valid types in our dataset.

In [ ]:
# We print the data types of the columns
print("Data types: ", df_MCF7_unf.dtypes)

Data types:  "output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam"    int64
"output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam"    int64
"output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam"    int64
"output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam"      int64
"output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam"      int64
                                                              ...  
"output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam"    int64
"output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam"    int64
"output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam"    int64
"output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam"    int64
"output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam"    int64
Length: 383, dtype: object
In [ ]:
# We create a dataframe from the original one with just the non-numeric columns to see if there are indeed non numeric values
non_numeric_df__MCF7_unf = df_MCF7_unf.select_dtypes(exclude=['int64', 'float64'])

# Print the data types of the non-numeric columns
print("Non-numeric data types: ", non_numeric_df__MCF7_unf.dtypes)

Non-numeric data types:  Series([], dtype: object)

Apart of numeric values, there are not other types of values in our dataset.

In [ ]:
# We now perform an additional step to explicitly check for NaN values in the dataframe
nan_df_MCF7_unf = df_MCF7_unf.isna()

# Print the columns that contain NaN values
print("Columns with NaN values: ", nan_df_MCF7_unf.any())

Columns with NaN values:  "output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam"    False
"output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam"    False
"output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam"    False
"output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam"      False
"output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam"      False
                                                              ...  
"output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam"    False
"output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam"    False
"output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam"    False
"output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam"    False
"output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam"    False
Length: 383, dtype: bool
In [ ]:
print(df_MCF7_unf.isnull().sum()) # we return the sum of null values in our dataset

"output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam"    0
"output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam"    0
"output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam"    0
"output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam"      0
"output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam"      0
                                                             ..
"output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam"    0
"output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam"    0
"output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam"    0
"output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam"    0
"output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam"    0
Length: 383, dtype: int64

We see that the sum of all null value in our dataset for each cell is zero, therefore we don't need to do any additional change to our dataset.

In [ ]:
# Also trying to drop the rows with missing values (but we see again that there are not missing values since the shape of the dataset is the same)
df_MCF7_unf = df_MCF7_unf.dropna()
df_MCF7_unf.shape
Out[ ]:
(22934, 383)
In [ ]:
df_MCF7_unf_without_duplicates = df_MCF7_unf.drop_duplicates()
df_MCF7_unf_without_duplicates.shape
Out[ ]:
(22905, 383)

Checking for duplicates we get a new dataframe with a shape of (22905, 383) that is different to the one of the original dataset (22934, 383). This seems to suggest that there are indeed duplicates in our dataset

¶

1.1.4) COMPUTATION AND VISUALIZATION OF MEAN, STD, QUANTILES, MAX AND MIN

We now look at some information in our unfiltered dataset. We start using the describe method which returns for each cells the mean, std, the 25,50,75 quantiles based on the genes

In [ ]:
# We start applying descriptive statistics to our dataset using the method .describe()
df_MCF7_unf_summary1 = df_MCF7_unf.describe()
df_MCF7_unf_summary1
Out[ ]:
"output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" "output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" "output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" "output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam" "output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam" "output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam" "output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam" "output.STAR.1_A7_Hypo_S25_Aligned.sortedByCoord.out.bam" ... "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" "output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam" "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam"
count 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 ... 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000
mean 40.817651 0.012253 86.442400 1.024636 14.531351 56.213613 75.397183 62.767725 67.396747 2.240734 ... 17.362562 42.080230 34.692422 32.735284 21.992718 17.439391 49.242784 61.545609 68.289352 62.851400
std 465.709940 0.207726 1036.572689 6.097362 123.800530 503.599145 430.471519 520.167576 459.689019 25.449630 ... 193.153757 256.775704 679.960908 300.291051 153.441647 198.179666 359.337479 540.847355 636.892085 785.670341
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
75% 17.000000 0.000000 5.000000 0.000000 7.000000 23.000000 39.000000 35.000000 38.000000 1.000000 ... 9.000000 30.000000 0.000000 17.000000 12.000000 9.000000 27.000000 30.000000 38.000000 33.000000
max 46744.000000 14.000000 82047.000000 289.000000 10582.000000 46856.000000 29534.000000 50972.000000 36236.000000 1707.000000 ... 17800.000000 23355.000000 81952.000000 29540.000000 12149.000000 19285.000000 28021.000000 40708.000000 46261.000000 68790.000000

8 rows × 383 columns

We can start to see some interesting facts about our dataset:

  • there are many cells that have the mean smaller then the maximum value (e.g in the first cell we can see the mean equal to almost 41 and the maximum value be 46744) and this seems to tell us that a lot of genes in our cells are not expressed while other are extremely characteristic.
  • there are also some cells which have mean almost equal zero and this tells us that probably they don't have so much influence in the entire scenario (maybe because these cells will die or for other factors which are kind of domain questions)

Now we go deep into our analysis plotting the distribution of the mean in the cells with an histogram. The plot shows us that there are two main peaks: one between zero and ten, so there are a lot of cells with a mean of almost zero and therefore very few genes expressed, and another peak around 50. Therefore most cells have a mean of 0 or of 50.

Moreover the count of the means follow a bimodal distribution.

In [ ]:
# Now let's try to represent through histograms the mean of the cells and how many times the different values appear
sns.displot(df_MCF7_unf_summary1.iloc[1], bins=70, color="green")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

We try to achieve the same plotting the distribution of the standard deviation in the cells with an histogram. We can see again two peaks, the first again around 0 and 10 and the second between 350 and 500. The standard deviation is not constant over the cells and there is also some dispersion in the dataset.

In [ ]:
sns.displot(df_MCF7_unf_summary1.iloc[2], bins=70, color="skyblue")
plt.show()
# the cells with high std are the ones with high number of genes expressed

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Now we plot histograms of 25, 50, 75-percentile of the distribution of all the cells and we notice the following things:

  • in the 25-percentile we have all cells with zero value (suggesting that 25% of genes in each cell has value equal zero)
  • in the 50-percentule we have most cells with zero value (sugggesting that 50% of genes in each cell has value equal zero)
  • in the 75% we have a peak at zero (suggesting most cells have 75% of genes with values equla zero), and another one around 30/40 (suggesting some cells have among the 75% of the genes with a value equal or smaller 30).

Therefore one hypothesis that can be formulated at this point is that the data we are analyzing now is sparse (so mostly formed by zero values) and not-symmetrical.

In [ ]:
# See what happens with 75% quartile
sns.displot(data = df_MCF7_unf_summary1.iloc[6], bins=50, color="red")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
# See what happens with 25% quartile
sns.displot(data = df_MCF7_unf_summary1.iloc[4], bins=50, color="orange")
plt.show()
 # at least 25% of the genes have a value of 0 in all cells, interesting

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
# See what happens with 50% quartile
sns.displot(data = df_MCF7_unf_summary1.iloc[5], bins=50, color="purple")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Finally, we represent the violin plot of all the cells (it is a little bit chaotic since there are many samples) to see how the distribution of each cell is concentrated.

Some of the samples have a distribution which is incredibly concentrated near zero (they are the ones with very short lines) while there are others represented by long line which have a wide range of gene expression levels, from very low to very high (in particular the length of these lines shows how far the most extreme values are from the median).

In [ ]:
plt.figure(figsize=(16,4))
plot=sns.violinplot(data=df_MCF7_unf,palette="Set3",cut=0)
plt.show()
No description has been provided for this image

¶

1.1.5) CHECK OF OUTLIERS

We now try to find the outliers, eliminate them from the dataset and see what changes respect to the original situation

In [ ]:
# Computations of interquartile range
Q1 = df_MCF7_unf.quantile(0.25)
Q3 = df_MCF7_unf.quantile(0.75)
IQR = Q3 - Q1
print(IQR)

"output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam"    17.0
"output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam"     0.0
"output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam"     5.0
"output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam"       0.0
"output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam"       7.0
                                                              ... 
"output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam"     9.0
"output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam"    27.0
"output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam"    30.0
"output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam"    38.0
"output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam"    33.0
Length: 383, dtype: float64
In [ ]:
df_MCF7_noOut = df_MCF7_unf[~((df_MCF7_unf < (Q1 - 1.5 * IQR)) |(df_MCF7_unf > (Q3 + 1.5 * IQR))).any(axis=1)]
df_MCF7_noOut.shape
Out[ ]:
(6435, 383)
In [ ]:
df_MCF7_noOut.head(3)
Out[ ]:
"output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" "output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" "output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" "output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam" "output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam" "output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam" "output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam" "output.STAR.1_A7_Hypo_S25_Aligned.sortedByCoord.out.bam" ... "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" "output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam" "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam"
"MIR6859-1" 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
"CICP3" 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
"FAM87B" 0 0 0 0 0 0 0 0 2 0 ... 0 1 0 0 0 0 0 0 0 0

3 rows × 383 columns

We can notice that after having removed the outliers we have remained with a smaller dataset (precisely the shape is just of (6435,383)) and most of them are zero. The reason is because actually the genes carry most of information were the ones in the last quartile.

Therefore we now come back to the original situation and investigate the sparsity of the data.

¶

1.1.6) SPARSITY OF DATA

We now represent the dataframe as a simple matrix and look at how many zero we have. Then we compute the percentage of the sparsity on our dataset (we basically subtract to one the number_of_non_zero_genes/total_number of genes)

In [ ]:
# We proceed in the following way. We start representing the dataframe as a simple matrix to see how many zero we have 
# then we compute the percentage of the sparsity on our dataset doing 1 - number_of_non_zero_genes / total_number of genes

df_MCF7_unf_Matrix = np.array(df_MCF7_unf)
print("Matrix useful for the sparsity check: \n", df_MCF7_unf_Matrix)

Matrix useful for the sparsity check: 
 [[ 0  0  0 ...  1  0  1]
 [ 0  0  0 ...  0  0  0]
 [ 1  0  0 ...  1  4  5]
 ...
 [ 0  0  0 ...  0  2  2]
 [ 5  0  0 ...  2 28 11]
 [ 8  0  0 ...  0  1  4]]
In [ ]:
Sparsity_matrix_MCF7 = 1 - count_nonzero(df_MCF7_unf_Matrix)/ df_MCF7_unf_Matrix.size
Sparsity_matrix_MCF7
Out[ ]:
0.6021531646834907

As we mentioned before the dataframe is highly sparse, infact the percentage of sparsity is more then 60% of the total dataframe.

¶

1.1.7) DATASET TRANSPOSITION AND APPLICATION OF STATISTICAL TOOLS

We now repeat the same steps (representation of means, std, etc) using the dataset transposed in order to see which genes are highly expressed with respect to the others

In [ ]:
df_MCF7_unf_transposed = df_MCF7_unf.transpose() # we transpose the dataset in order to get cells 
# as rows and genes as columns
df_MCF7_unf_transposed
Out[ ]:
"WASH7P" "MIR6859-1" "WASH9P" "OR4F29" "MTND1P23" "MTND2P28" "MTCO1P12" "MTCO2P12" "MTATP8P1" "MTATP6P1" ... "MT-TH" "MT-TS2" "MT-TL2" "MT-ND5" "MT-ND6" "MT-TE" "MT-CYB" "MT-TT" "MT-TP" "MAFIP"
"output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" 0 0 1 0 0 2 2 0 0 29 ... 0 0 0 505 147 4 270 0 5 8
"output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" 0 0 0 0 0 0 0 0 0 0 ... 1 1 0 1 0 0 1 0 0 0
"output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" 0 0 0 0 0 1 1 1 0 12 ... 0 0 0 1 0 0 76 0 0 0
"output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" 0 0 0 0 0 0 0 0 0 7 ... 1 0 0 44 8 0 66 0 1 0
"output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" 0 0 0 0 0 0 0 0 0 68 ... 0 0 0 237 31 3 727 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
"output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" 0 0 0 0 0 0 1 0 0 49 ... 0 0 1 341 46 1 570 0 0 0
"output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" 0 0 1 0 0 2 5 5 0 370 ... 0 0 2 1612 215 6 3477 3 7 6
"output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" 1 0 1 0 0 7 0 0 0 33 ... 0 0 0 62 20 0 349 0 2 0
"output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" 0 0 4 1 0 29 4 0 0 228 ... 3 0 2 1934 575 7 2184 2 28 1
"output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam" 1 0 5 0 0 5 3 0 0 71 ... 5 2 3 1328 490 4 1149 2 11 4

383 rows × 22934 columns

In [ ]:
df_MCF7_unf_summary2 = df_MCF7_unf_transposed.describe() # as before we applied the describe method to get the mean, std etc for each gene across all the cells
df_MCF7_unf_summary2
Out[ ]:
"WASH7P" "MIR6859-1" "WASH9P" "OR4F29" "MTND1P23" "MTND2P28" "MTCO1P12" "MTCO2P12" "MTATP8P1" "MTATP6P1" ... "MT-TH" "MT-TS2" "MT-TL2" "MT-ND5" "MT-ND6" "MT-TE" "MT-CYB" "MT-TT" "MT-TP" "MAFIP"
count 383.000000 383.000000 383.000000 383.00000 383.000000 383.000000 383.000000 383.000000 383.000000 383.000000 ... 383.000000 383.000000 383.000000 383.000000 383.000000 383.000000 383.00000 383.000000 383.000000 383.000000
mean 0.133159 0.026110 1.344648 0.05483 0.049608 6.261097 4.681462 0.524804 0.073107 222.054830 ... 1.060052 0.443864 3.146214 1016.477807 204.600522 5.049608 2374.97389 2.083551 5.626632 1.749347
std 0.618664 0.249286 2.244543 0.31477 0.229143 7.565749 6.232649 0.980857 0.298131 262.616874 ... 1.990566 1.090827 4.265352 1009.444811 220.781927 6.644302 2920.39000 3.372714 7.511180 3.895204
min 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 23.000000 ... 0.000000 0.000000 0.000000 172.000000 30.500000 0.000000 216.50000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 0.00000 0.000000 3.000000 2.000000 0.000000 0.000000 98.000000 ... 0.000000 0.000000 1.000000 837.000000 152.000000 3.000000 785.00000 0.000000 3.000000 0.000000
75% 0.000000 0.000000 2.000000 0.00000 0.000000 10.000000 7.000000 1.000000 0.000000 370.500000 ... 1.000000 0.000000 5.000000 1549.000000 294.000000 7.000000 4059.00000 3.000000 8.000000 2.000000
max 9.000000 4.000000 20.000000 3.00000 2.000000 45.000000 36.000000 6.000000 2.000000 1662.000000 ... 15.000000 8.000000 22.000000 8115.000000 2002.000000 46.000000 16026.00000 22.000000 56.000000 32.000000

8 rows × 22934 columns

We can see that the mean of most genes is zero (therefore most genes are zero across all the cells), the std is zero as well in most of the genes, and the for the 75-percentile we have just few genes that are not zero in the 75 percentile. (this time we don't plot the 25/50-percentile since we would get again mainly zeros)

In [ ]:
# We now plot more or less the same graph of before to see the expression of individual genes across cells 
sns.displot(df_MCF7_unf_summary2.iloc[1], bins=600, color="green")
plt.xlim(0, 800) # we limit the x axis to 800 in order to avoid small pictures
# this is also allowed because the values of x-axis stop before
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
sns.displot(df_MCF7_unf_summary2.iloc[2], bins=600, color="skyblue")
plt.xlim(0, 800) # we limit the x axis to 800 in order to avoid small pictures
# this is also allowed because the values of x-axis stop before
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
#75% quantile
sns.displot(df_MCF7_unf_summary2.iloc[6], bins=600, color="purple")
plt.xlim(0, 800) # we limit the x axis to 800 in order to avoid small pictures
# this is also allowed because the values of x-axis stop before
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Also from this persepctive we can see that most genes have not value higher then zero. We can avoid to represent again the 25% and 50% quantiles

¶

1.1.8) CHECK OF NORMALIZATION OF DATA USING SKEWENESS AND KURTOSIS

Now we analyze the skewness and Kurtosis for each single cell to verify if it is normalized or not ( since most of these values are not near 0, we can deudce that most cells have a different distribution from the normal one).

In [ ]:
from scipy.stats import kurtosis, skew
cnames = list(df_MCF7_unf.columns)
colN = np.shape(df_MCF7_unf)[1]
colN
df_skew_cells = []
for i in range(colN) :     
     v_df = df_MCF7_unf[cnames[i]]
     df_skew_cells += [skew(v_df)]   
df_skew_cells
sns.histplot(df_skew_cells,bins=100, color='blue', kde=True)
plt.xlabel('Skewness of single cells expression profiles - original df')
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
df_kurt_cells = []
for i in range(colN) :     
     v_df = df_MCF7_unf[cnames[i]]
     df_kurt_cells += [kurtosis(v_df)]   
df_kurt_cells
sns.histplot(df_kurt_cells,bins=100, color='green', kde=True)
plt.xlabel('Kurtosis of single cells expression profiles - original df')
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
# Now we compute the difference between the two distributions and our distributions created randomly
xnorm = np.random.normal(0, 2, 10000000)   # create random values based on a normal distribution
xnorm 

print( "Excess kurtosis of normal distribution: ",  kurtosis(xnorm) )
print( "Skewness of normal distribution: ", skew(xnorm) )

Excess kurtosis of normal distribution:  0.000966120288296235
Skewness of normal distribution:  -0.0004663586529028709

¶

1.1.9) DATA TRANSORMATIONS USING LOG 2

We now try to transform the data, computing the log base 2 across all the dataset in order to see if this transformation can be a good normalization

In [ ]:
# First we add 1 to all the dataset to avoid log(0) and then we compute the log of the dataset
df_MCF7_unf_log = (df_MCF7_unf + 1).apply(np.log2)
df_MCF7_unf_log
Out[ ]:
"output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" "output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" "output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" "output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam" "output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam" "output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam" "output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam" "output.STAR.1_A7_Hypo_S25_Aligned.sortedByCoord.out.bam" ... "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" "output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam" "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam"
"WASH7P" 0.000000 0.0 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000
"MIR6859-1" 0.000000 0.0 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
"WASH9P" 1.000000 0.0 0.000000 0.000000 0.000000 1.00000 3.459432 1.000000 0.000000 0.000000 ... 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000 2.321928 2.584963
"OR4F29" 0.000000 0.0 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000
"MTND1P23" 0.000000 0.0 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
"MT-TE" 2.321928 0.0 0.000000 0.000000 2.000000 2.00000 0.000000 0.000000 3.906891 1.000000 ... 0.000000 2.321928 3.700440 2.321928 0.000000 1.000000 2.807355 0.000000 3.000000 2.321928
"MT-CYB" 8.082149 1.0 6.266787 6.066089 9.507795 11.40833 13.187197 11.667999 12.956739 4.954196 ... 7.906891 11.890264 13.639567 11.144658 10.418907 9.157347 11.764042 8.451211 11.093418 10.167418
"MT-TT" 0.000000 0.0 0.000000 0.000000 0.000000 1.00000 1.000000 1.000000 2.321928 0.000000 ... 0.000000 3.000000 2.321928 1.584963 0.000000 0.000000 2.000000 0.000000 1.584963 1.584963
"MT-TP" 2.584963 0.0 0.000000 1.000000 0.000000 1.00000 1.000000 2.321928 1.584963 0.000000 ... 0.000000 3.906891 5.832890 3.584963 1.584963 0.000000 3.000000 1.584963 4.857981 3.584963
"MAFIP" 3.169925 0.0 0.000000 0.000000 0.000000 0.00000 0.000000 1.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 1.584963 1.000000 0.000000 2.807355 0.000000 1.000000 2.321928

22934 rows × 383 columns

In [ ]:
# We now apply again the method .describe() to see how information as mean/std is changed
df_MCF7_unf_log_summary = df_MCF7_unf_log.describe()
df_MCF7_unf_log_summary
Out[ ]:
"output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" "output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" "output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" "output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam" "output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam" "output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam" "output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam" "output.STAR.1_A7_Hypo_S25_Aligned.sortedByCoord.out.bam" ... "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" "output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam" "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam"
count 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 ... 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000
mean 1.889979 0.009665 1.732075 0.408770 1.563776 2.174915 2.539393 2.600671 2.502254 0.627249 ... 1.659217 2.371224 0.511852 1.972339 1.744485 1.624858 2.145188 2.221317 2.368192 2.298742
std 2.743667 0.115893 3.060936 0.932712 2.158735 2.936550 3.166716 3.027012 3.107430 1.184292 ... 2.207388 2.864281 1.880059 2.656387 2.428364 2.241559 2.939852 2.998384 3.098435 2.993186
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
75% 4.169925 0.000000 2.584963 0.000000 3.000000 4.584963 5.321928 5.169925 5.285402 1.000000 ... 3.321928 4.954196 0.000000 4.169925 3.700440 3.321928 4.807355 4.954196 5.285402 5.087463
max 15.512524 3.906891 16.324181 8.179909 13.369461 15.515977 14.850138 15.637446 15.145176 10.738092 ... 14.119671 14.511506 16.322509 14.850431 13.568669 14.235266 14.774272 15.313060 15.497540 16.069932

8 rows × 383 columns

We plot again the histograms of mean, standard deviation and third quantile. We notice that we have smaller intervals of values.

In [ ]:
# we now plot more or less some charts (at least mean, std and 75% quartile)
sns.displot(data = df_MCF7_unf_log_summary.iloc[1], bins=50, color="green")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
sns.displot(data = df_MCF7_unf_log_summary.iloc[2], bins=50, color="skyblue")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
sns.displot(data = df_MCF7_unf_log_summary.iloc[6], bins=50, color="red")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Finally we plot again the skeweness and the kurtosis to understand if the transformed data are now normalized.

In [ ]:
# We apply again skeweness and kurtosis to the log transformed dataset
from scipy.stats import kurtosis, skew
cnames = list(df_MCF7_unf_log.columns)
colN = np.shape(df_MCF7_unf_log)[1]
colN
df_skew_cells = []
for i in range(colN) :     
     v_df = df_MCF7_unf_log[cnames[i]]
     df_skew_cells += [skew(v_df)]   
  #  df_skew_cells += [df[cnames[i]].skew()]
df_skew_cells
sns.histplot(df_skew_cells,bins=100, color='blue', kde=True)
plt.xlabel('Skewness of single cells expression profiles - original df')
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
df_kurt_cells = []
for i in range(colN) :     
     v_df = df_MCF7_unf_log[cnames[i]]
     df_kurt_cells += [kurtosis(v_df)]   
 #   df_kurt_cells += [df[cnames[i]].kurt()]
df_kurt_cells
sns.histplot(df_kurt_cells,bins=100, color='green', kde=True)
plt.xlabel('Kurtosis of single cells expression profiles - original df')
plt.show()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[357], line 2
      1 df_kurt_cells = []
----> 2 for i in range(colN) :     
      3      v_df = df_MCF7_unf_log[cnames[i]]
      4      df_kurt_cells += [kurtosis(v_df)]   

NameError: name 'colN' is not defined

As final observation we can notice that most data has value zero in both skeweness and kurtosis telling us that there is no asymmetry and the data is more normalized.

¶

1.1.10) Study of PCA

¶

1.2) HCC1806

¶

1.2.1) READING THE META-DATASET USEFUL SINCE CONTAINS SOME INSIGHTS

We start downloading and opening the meta_data file of HCC1806

In [ ]:
df_HCC1806_unf_meta = pd.read_csv("SmartSeq/HCC1806_SmartS_MetaData.tsv",delimiter="\t",engine='python',index_col=0)
print("Dataframe dimensions:", np.shape(df_HCC1806_unf_meta))
print("First column: ", df_HCC1806_unf_meta.iloc[ : , 0])

Dataframe dimensions: (243, 8)
First column:  Filename
output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam    HCC1806
output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam     HCC1806
output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam       HCC1806
output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam      HCC1806
output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam        HCC1806
                                                                         ...   
output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam    HCC1806
output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam    HCC1806
output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam      HCC1806
output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam     HCC1806
output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam     HCC1806
Name: Cell Line, Length: 243, dtype: object
In [ ]:
df_HCC1806_unf_meta.shape
Out[ ]:
(243, 8)

As we can see the file has a shape of 243 rows and 8 columns and is made in the following way: the rows represent the cells while the columns give us information about the experiment, e.g. we have the cell line, the plate, the position of the cell, the condition (hypoxia or Normoxia), and other informations as the comment or tag.

Moreover the 243 cells tell us that in the main dataset we will have 243 cells (in that case as columns)

In [ ]:
df_HCC1806_unf_meta.head(243)
Out[ ]:
Cell Line PCR Plate Pos Condition Hours Cell name PreprocessingTag ProcessingComments
Filename
output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam HCC1806 1 A10 Normo 24 S123 Aligned.sortedByCoord.out.bam STAR,FeatureCounts
output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam HCC1806 1 A12 Normo 24 S26 Aligned.sortedByCoord.out.bam STAR,FeatureCounts
output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam HCC1806 1 A1 Hypo 24 S97 Aligned.sortedByCoord.out.bam STAR,FeatureCounts
output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam HCC1806 1 A2 Hypo 24 S104 Aligned.sortedByCoord.out.bam STAR,FeatureCounts
output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam HCC1806 1 A3 Hypo 24 S4 Aligned.sortedByCoord.out.bam STAR,FeatureCounts
... ... ... ... ... ... ... ... ...
output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam HCC1806 4 H10 Normo 24 S210 Aligned.sortedByCoord.out.bam STAR,FeatureCounts
output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam HCC1806 4 H11 Normo 24 S214 Aligned.sortedByCoord.out.bam STAR,FeatureCounts
output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam HCC1806 4 H2 Hypo 24 S199 Aligned.sortedByCoord.out.bam STAR,FeatureCounts
output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam HCC1806 4 H7 Normo 24 S205 Aligned.sortedByCoord.out.bam STAR,FeatureCounts
output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam HCC1806 4 H9 Normo 24 S236 Aligned.sortedByCoord.out.bam STAR,FeatureCounts

243 rows × 8 columns

In [ ]:
df_HCC1806_unf_meta.describe()
Out[ ]:
PCR Plate Hours
count 243.000000 243.0
mean 2.411523 24.0
std 1.081107 0.0
min 1.000000 24.0
25% 1.000000 24.0
50% 2.000000 24.0
75% 3.000000 24.0
max 4.000000 24.0

We see that every cell stays in a defined PCR plate for 24 hours (mean is 24, std is 0)

In [ ]:
df_HCC1806_unf_meta["Condition"].value_counts().plot(kind='bar')
number_of_cells=df_HCC1806_unf_meta["Condition"].value_counts()
print(number_of_cells)

Condition
Hypo     126
Normo    117
Name: count, dtype: int64
No description has been provided for this image

Hypoxic cells are slightly more than Normoxyc ones (51.85% of the total meta dataset)

In [ ]:
df_HCC1806_unf_meta.hist(column='PCR Plate', bins=30)
number_of_plates=df_HCC1806_unf_meta["PCR Plate"].value_counts()
print(number_of_plates)

PCR Plate
3    68
1    64
2    63
4    48
Name: count, dtype: int64
No description has been provided for this image
In [ ]:
df_HCC1806_unf_meta.hist(column='PCR Plate', by='Condition', bins=30)

# Filter the DataFrame for "Hypo" and "Normo" elements
hypo_elements = df_HCC1806_unf_meta[df_HCC1806_unf_meta['Condition'] == 'Hypo']
normo_elements = df_HCC1806_unf_meta[df_HCC1806_unf_meta['Condition'] == 'Normo']

# Count the number of "Hypo" and "Normo" elements in each PCR Plate
hypo_counts = hypo_elements['PCR Plate'].value_counts()
normo_counts = normo_elements['PCR Plate'].value_counts()


# Print the counts for each PCR Plate
for plate in sorted(set(df_HCC1806_unf_meta['PCR Plate'])):
    hypo_count = hypo_counts.get(plate, 0)
    normo_count = normo_counts.get(plate, 0)
    print(f"PCR Plate {plate}: Hypo = {hypo_count}, Normo = {normo_count}")

PCR Plate 1: Hypo = 32, Normo = 32
PCR Plate 2: Hypo = 34, Normo = 29
PCR Plate 3: Hypo = 39, Normo = 29
PCR Plate 4: Hypo = 21, Normo = 27
No description has been provided for this image

On plate 1 Hypoxic and Normoxic cells are equally distributed

On plate 2 there are a bit more Hypoxic cells (54%)

Also on plate 3 there are more Hypoxic cells (57.4%)

Instead, on plate 4 there are slightly more Normoxic cells (56.25%)

¶

1.2.2) OPENING THE MAIN DATASET: UNFILTERED HCC1806 DATASET PRODUCED USING THE SMART SEQUENCING TECHNOLOGY

We start now opening the HCC1806 SmartSeq unfiltered dataset since we are interested in analyzing it.

In [ ]:
df_HCC1806_unf = pd.read_csv("SmartSeq/HCC1806_SmartS_Unfiltered_Data.txt",delimiter="\ ",engine='python',index_col=0)
print("Dataframe dimensions:", np.shape(df_HCC1806_unf))
print("First column: ", df_HCC1806_unf.iloc[ : , :])

Dataframe dimensions: (23396, 243)
First column:              "output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                    0                        
"CICP27"                                                    0                        
"DDX11L17"                                                  0                        
"WASH9P"                                                    0                        
"OR4F29"                                                    2                        
...                                                       ...                        
"MT-TE"                                                    22                        
"MT-CYB"                                                 4208                        
"MT-TT"                                                    26                        
"MT-TP"                                                    66                        
"MAFIP"                                                     0                        

            "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                    0                       
"CICP27"                                                    0                       
"DDX11L17"                                                  0                       
"WASH9P"                                                    0                       
"OR4F29"                                                    0                       
...                                                       ...                       
"MT-TE"                                                    43                       
"MT-CYB"                                                 6491                       
"MT-TT"                                                    62                       
"MT-TP"                                                    71                       
"MAFIP"                                                     4                       

            "output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                    0                     
"CICP27"                                                    0                     
"DDX11L17"                                                  0                     
"WASH9P"                                                    0                     
"OR4F29"                                                    0                     
...                                                       ...                     
"MT-TE"                                                     0                     
"MT-CYB"                                                   25                     
"MT-TT"                                                     0                     
"MT-TP"                                                     1                     
"MAFIP"                                                     0                     

            "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                    0                      
"CICP27"                                                    0                      
"DDX11L17"                                                  0                      
"WASH9P"                                                    0                      
"OR4F29"                                                    0                      
...                                                       ...                      
"MT-TE"                                                     0                      
"MT-CYB"                                                 4819                      
"MT-TT"                                                    11                      
"MT-TP"                                                     3                      
"MAFIP"                                                     7                      

            "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                    0                    
"CICP27"                                                    0                    
"DDX11L17"                                                  0                    
"WASH9P"                                                    0                    
"OR4F29"                                                    0                    
...                                                       ...                    
"MT-TE"                                                     0                    
"MT-CYB"                                                  310                    
"MT-TT"                                                     4                    
"MT-TP"                                                     9                    
"MAFIP"                                                     0                    

            "output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                    0                    
"CICP27"                                                    0                    
"DDX11L17"                                                  1                    
"WASH9P"                                                    0                    
"OR4F29"                                                    0                    
...                                                       ...                    
"MT-TE"                                                     3                    
"MT-CYB"                                                  695                    
"MT-TT"                                                     0                    
"MT-TP"                                                    14                    
"MAFIP"                                                     9                    

            "output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                    0                      
"CICP27"                                                    0                      
"DDX11L17"                                                  0                      
"WASH9P"                                                    0                      
"OR4F29"                                                    1                      
...                                                       ...                      
"MT-TE"                                                    47                      
"MT-CYB"                                                 2885                      
"MT-TT"                                                    41                      
"MT-TP"                                                    91                      
"MAFIP"                                                     0                      

            "output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                    0                     
"CICP27"                                                    0                     
"DDX11L17"                                                  0                     
"WASH9P"                                                    0                     
"OR4F29"                                                    0                     
...                                                       ...                     
"MT-TE"                                                     4                     
"MT-CYB"                                                 1552                     
"MT-TT"                                                     9                     
"MT-TP"                                                    22                     
"MAFIP"                                                     4                     

            "output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                    0                       
"CICP27"                                                    0                       
"DDX11L17"                                                  0                       
"WASH9P"                                                    1                       
"OR4F29"                                                    0                       
...                                                       ...                       
"MT-TE"                                                     2                       
"MT-CYB"                                                  366                       
"MT-TT"                                                     2                       
"MT-TP"                                                     3                       
"MAFIP"                                                     2                       

            "output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                    0                       
"CICP27"                                                    0                       
"DDX11L17"                                                  0                       
"WASH9P"                                                    0                       
"OR4F29"                                                    0                       
...                                                       ...                       
"MT-TE"                                                     8                       
"MT-CYB"                                                 1829                       
"MT-TT"                                                     8                       
"MT-TP"                                                    30                       
"MAFIP"                                                     0                       

            ...  \
"WASH7P"    ...   
"CICP27"    ...   
"DDX11L17"  ...   
"WASH9P"    ...   
"OR4F29"    ...   
...         ...   
"MT-TE"     ...   
"MT-CYB"    ...   
"MT-TT"     ...   
"MT-TP"     ...   
"MAFIP"     ...   

            "output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                    0                        
"CICP27"                                                    0                        
"DDX11L17"                                                  0                        
"WASH9P"                                                    0                        
"OR4F29"                                                    0                        
...                                                       ...                        
"MT-TE"                                                    24                        
"MT-CYB"                                                 1119                        
"MT-TT"                                                    48                        
"MT-TP"                                                   119                        
"MAFIP"                                                     2                        

            "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                    0                      
"CICP27"                                                    0                      
"DDX11L17"                                                  0                      
"WASH9P"                                                    0                      
"OR4F29"                                                    0                      
...                                                       ...                      
"MT-TE"                                                    15                      
"MT-CYB"                                                 1429                      
"MT-TT"                                                    31                      
"MT-TP"                                                    52                      
"MAFIP"                                                     0                      

            "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                    0                      
"CICP27"                                                    0                      
"DDX11L17"                                                  0                      
"WASH9P"                                                    0                      
"OR4F29"                                                    0                      
...                                                       ...                      
"MT-TE"                                                    15                      
"MT-CYB"                                                  808                      
"MT-TT"                                                     3                      
"MT-TP"                                                    11                      
"MAFIP"                                                     2                      

            "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                    0                      
"CICP27"                                                    0                      
"DDX11L17"                                                  0                      
"WASH9P"                                                    0                      
"OR4F29"                                                    0                      
...                                                       ...                      
"MT-TE"                                                     4                      
"MT-CYB"                                                  999                      
"MT-TT"                                                     8                      
"MT-TP"                                                    22                      
"MAFIP"                                                     1                      

            "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                    0                       
"CICP27"                                                    0                       
"DDX11L17"                                                  0                       
"WASH9P"                                                    0                       
"OR4F29"                                                    0                       
...                                                       ...                       
"MT-TE"                                                     4                       
"MT-CYB"                                                  916                       
"MT-TT"                                                     5                       
"MT-TP"                                                    15                       
"MAFIP"                                                     1                       

            "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                    0                        
"CICP27"                                                    0                        
"DDX11L17"                                                  0                        
"WASH9P"                                                    1                        
"OR4F29"                                                    0                        
...                                                       ...                        
"MT-TE"                                                    26                        
"MT-CYB"                                                 3719                        
"MT-TT"                                                    42                        
"MT-TP"                                                    48                        
"MAFIP"                                                     3                        

            "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                    0                        
"CICP27"                                                    0                        
"DDX11L17"                                                  0                        
"WASH9P"                                                    0                        
"OR4F29"                                                    0                        
...                                                       ...                        
"MT-TE"                                                     1                        
"MT-CYB"                                                  984                        
"MT-TT"                                                     1                        
"MT-TP"                                                    18                        
"MAFIP"                                                     0                        

            "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                    0                      
"CICP27"                                                    0                      
"DDX11L17"                                                  0                      
"WASH9P"                                                    1                      
"OR4F29"                                                    0                      
...                                                       ...                      
"MT-TE"                                                     4                      
"MT-CYB"                                                 2256                      
"MT-TT"                                                    15                      
"MT-TP"                                                    36                      
"MAFIP"                                                     2                      

            "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam"  \
"WASH7P"                                                    0                       
"CICP27"                                                    0                       
"DDX11L17"                                                  0                       
"WASH9P"                                                    0                       
"OR4F29"                                                    0                       
...                                                       ...                       
"MT-TE"                                                     4                       
"MT-CYB"                                                  981                       
"MT-TT"                                                     6                       
"MT-TP"                                                     8                       
"MAFIP"                                                     1                       

            "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam"  
"WASH7P"                                                    0                      
"CICP27"                                                    0                      
"DDX11L17"                                                  0                      
"WASH9P"                                                    0                      
"OR4F29"                                                    0                      
...                                                       ...                      
"MT-TE"                                                    20                      
"MT-CYB"                                                 2039                      
"MT-TT"                                                    34                      
"MT-TP"                                                    79                      
"MAFIP"                                                     5                      

[23396 rows x 243 columns]
In [ ]:
df_HCC1806_unf.head(243)
Out[ ]:
"output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam" ... "output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam"
"WASH7P" 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
"CICP27" 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
"DDX11L17" 0 0 0 0 0 1 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
"WASH9P" 0 0 0 0 0 0 0 0 1 0 ... 0 0 0 0 0 1 0 1 0 0
"OR4F29" 2 0 0 0 0 0 1 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
"RPL12P14" 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
"UQCRHL" 14 12 2 16 3 1 15 17 3 13 ... 4 11 7 2 3 4 0 11 1 9
"SPEN" 75 519 11 0 23 0 322 21 137 5 ... 35 123 13 39 46 15 11 81 35 34
"ZBTB17" 60 0 12 0 35 0 198 0 0 0 ... 73 0 0 0 18 211 24 69 20 5
"CLCNKA" 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0

243 rows × 243 columns

In [ ]:
df_HCC1806_unf.shape # this gives us the shape of the dataframe
Out[ ]:
(23396, 243)

The HCC1806 SmartSeq unfiltered dataset is made of 23396 rows representing specific genes and 243 columns representing the cells (already available in the meta dataset). Therefore each entry tells us how much of every gene is expressed in every cells. Moreover, each cell has in its name written the state in which it was under (normoxia / hypoxia), and such information was given as a separate column in the meta dataset.

In [ ]:
#We return the indexes of the dataframe, which are the gene symbols
gene_symbls = df_HCC1806_unf.index
print("Dataframe indexes: ", gene_symbls)

    

Dataframe indexes:  Index(['"WASH7P"', '"CICP27"', '"DDX11L17"', '"WASH9P"', '"OR4F29"',
       '"MTND1P23"', '"MTND2P28"', '"MTCO1P12"', '"MTCO2P12"', '"MTATP8P1"',
       ...
       '"MT-TH"', '"MT-TS2"', '"MT-TL2"', '"MT-ND5"', '"MT-ND6"', '"MT-TE"',
       '"MT-CYB"', '"MT-TT"', '"MT-TP"', '"MAFIP"'],
      dtype='object', length=23396)

¶

1.2.3) CHECKING MISSING AND NON VALID VALUES IN OUR DATASET

We now perform some operations to see if there are non numeric or non valid types in our dataset.

In [ ]:
# We now print all data types of our dataframe 
print("Data types: ", df_HCC1806_unf.dtypes)

Data types:  "output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam"    int64
"output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam"     int64
"output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam"       int64
"output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam"      int64
"output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam"        int64
                                                                          ...  
"output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam"    int64
"output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam"    int64
"output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam"      int64
"output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam"     int64
"output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam"     int64
Length: 243, dtype: object
In [ ]:
# We create a dataframe from the original one with just the non-numeric columns to see if there are indeed non numeric values
non_numeric_df_HCC1806_unf = df_HCC1806_unf.select_dtypes(exclude=['int64', 'float64'])

# Print the data types of the non-numeric columns
print("Non-numeric data types: ", non_numeric_df_HCC1806_unf.dtypes)

Non-numeric data types:  Series([], dtype: object)

Apart of numeric values, there are not other types of values in our dataset.

In [ ]:
# We now perform an additional step to explicitly check for NaN values in the dataframe
nan_df_HCC1806 = df_HCC1806_unf.isna()

# Print the columns that contain NaN values
print("Columns with NaN values: ", nan_df_HCC1806.any())

Columns with NaN values:  "output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam"    False
"output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam"     False
"output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam"       False
"output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam"      False
"output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam"        False
                                                                          ...  
"output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam"    False
"output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam"    False
"output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam"      False
"output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam"     False
"output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam"     False
Length: 243, dtype: bool
In [ ]:
print(df_HCC1806_unf.isnull().sum()) # we return the sum of null values in our dataset

"output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam"    0
"output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam"     0
"output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam"       0
"output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam"      0
"output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam"        0
                                                                         ..
"output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam"    0
"output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam"    0
"output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam"      0
"output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam"     0
"output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam"     0
Length: 243, dtype: int64

We see that the sum of all null value in our dataset for each cell is zero, therefore we don't need to do any additional change to our dataset.

In [ ]:
# We now try to drop missing values to see if indeed there are any of them
df_HCC1806_unf = df_HCC1806_unf.dropna()
df_HCC1806_unf.shape 
# we see that the shape of the dataframe is the same, meaning that there are no missing values in the dataset
Out[ ]:
(23396, 243)
In [ ]:
# No duplicates
df_HCC1806_unf_without_duplicates = df_HCC1806_unf.drop_duplicates()
df_HCC1806_unf_without_duplicates.shape
Out[ ]:
(23342, 243)

Checking for duplicates we get a new dataframe with a shape of (23343, 243) that is different to the one of the original dataset (23396, 243). This seems to suggest that there are indeed duplicates in our dataset

¶

1.2.4) COMPUTATION AND VISUALIZATION OF MEAN, STD, QUANTILES, MAX AND MIN

We now look at some information in our unfiltered HCC1806 dataset. We start using the describe method which returns for each cells the mean, std, the 25,50,75 quantiles based on the genes

In [ ]:
# We start applying descriptive statistics to our dataset using the method .describe()
df_HCC1806_unf_summary1 = df_HCC1806_unf.describe()
df_HCC1806_unf_summary1
Out[ ]:
"output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam" ... "output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam"
count 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 ... 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000
mean 99.565695 207.678278 9.694734 150.689007 35.700504 47.088434 152.799453 135.869422 38.363908 45.512139 ... 76.361771 105.566593 54.026116 29.763806 28.905411 104.740725 35.181569 108.197940 37.279962 76.303855
std 529.532443 981.107905 65.546050 976.936548 205.885369 545.367706 864.974182 870.729740 265.062493 366.704721 ... 346.659348 536.881574 344.068304 186.721266 135.474736 444.773045 170.872090 589.082268 181.398951 369.090274
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 1.000000 0.000000 0.000000 0.000000 0.000000 2.000000 0.000000 0.000000 0.000000 1.000000
75% 51.000000 125.000000 5.000000 40.000000 22.000000 17.000000 81.000000 76.000000 22.000000 18.000000 ... 56.000000 67.000000 29.000000 18.000000 19.000000 76.000000 24.000000 68.000000 22.000000 44.000000
max 35477.000000 69068.000000 6351.000000 70206.000000 17326.000000 47442.000000 43081.000000 62813.000000 30240.000000 35450.000000 ... 19629.000000 30987.000000 21894.000000 13457.000000 11488.000000 33462.000000 15403.000000 34478.000000 10921.000000 28532.000000

8 rows × 243 columns

As for the MCF7, also here we can see some interesting facts about our dataset:

  • there are many cells that have the mean smaller then the maximum value (e.g in the first cell we can see the mean equal to almost 100 and the maximum value be 35477) and this seems to tell us that a lot of genes in our cells are not expressed while other are extremely characteristic.
  • there are also some cells which have mean almost equal zero and this tells us that probably they don't have so much influence in the entire scenario (maybe because these cells will die or for other factors which are kind of domain questions)

Now we go deep into our analysis plotting again (as we did in the other dataset) the distribution of the mean in the cells with an histogram. The plot shows us that there are two main peaks: one near zero, so there are a lot of cells with a mean of almost zero and therefore very few genes expressed, and another peak between 30 and 40.

In [ ]:
# Now let's try to represent through histograms the mean of the cells and how many times the different values appear
sns.displot(df_HCC1806_unf_summary1.iloc[1], bins=70, color="green")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

We try to achieve the same plotting the distribution of the standard deviation in the cells with an histogram. We can see again two peaks, the first again around 0 and the second between 200 and 250. The standard deviation is not constant over the cells and there is also some dispersion in the dataset.

In [ ]:
sns.displot(df_HCC1806_unf_summary1.iloc[2], bins=70, color="skyblue")
plt.show()
# the cells with high std are the ones with high number of genes expressed

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Now we plot histograms of 25, 50, 75-percentile of the distribution of all the cells and we notice the following things:

  • in the 25-percentile we have all cells with zero value (suggesting that 25% of genes in each cell has value equal zero)
  • in the 50-percentule we have most cells with zero value (sugggesting that 50% of genes in each cell has value equal zero)
  • in the 75% we have an interesting scenario since it seems that in average this percentile is smaller then the mean, suggesting that most cells are zero values with some having very large values.
In [ ]:
# See what happens with 75% quartile
sns.displot(data = df_HCC1806_unf_summary1.iloc[6], bins=50, color="red")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
# See what happens with 25% quartile
sns.displot(data = df_HCC1806_unf_summary1.iloc[4], bins=50, color="purple")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
# See what happens with 50% quartile
sns.displot(data = df_HCC1806_unf_summary1.iloc[5], bins=50, color="purple")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Finally, we represent again the violin plot of all the cells (it is a little bit chaotic since there are many samples) to see how the distribution of each cell is concentrated.

Again Some of the samples have a distribution which is incredibly concentrated near zero (they are the ones with very short lines) while there are others represented by long line which have a wide range of gene expression levels, from very low to very high (in particular the length of these lines shows how far the most extreme values are from the median).

In [ ]:
plt.figure(figsize=(16,4))
plot=sns.violinplot(data= df_HCC1806_unf,palette="Set3",cut=0)
plt.show()
No description has been provided for this image

¶

1.2.5) CHECK OF OUTLIERS

We now try to find the outliers, eliminate them from the dataset and see what changes respect to the original situation

In [ ]:
# Computation of the inter quantile range.

Q1 = df_HCC1806_unf.quantile(0.25)
Q3 = df_HCC1806_unf.quantile(0.75)
IQR = Q3 - Q1
print(IQR)

"output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam"     51.0
"output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam"     125.0
"output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam"         5.0
"output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam"       40.0
"output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam"         22.0
                                                                          ...  
"output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam"     76.0
"output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam"     24.0
"output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam"       68.0
"output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam"      22.0
"output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam"      44.0
Length: 243, dtype: float64
In [ ]:
df_HCC1806_noOut = df_HCC1806_unf[~((df_HCC1806_unf < (Q1 - 1.5 * IQR)) |(df_HCC1806_unf > (Q3 + 1.5 * IQR))).any(axis=1)]
df_HCC1806_noOut.shape
#df_noOut.head(3)

# important: we get a shaper of (10815,243), it is quite important to note that we have removed a lot of data, since before we had (23396, 243)

# They don't look good. We have just zeros now. Simply removing outliers would not work here, as many genes/features would simply be 0 if we excluded the outliers. 
# So we need to proceed differently, as the outliers seem to be the values carrying the information in this case (this has been told by the professor)
Out[ ]:
(10815, 243)
In [ ]:
df_HCC1806_noOut.shape
Out[ ]:
(10815, 243)

Also in this case we can notice that after having removed the outliers we have remained with a smaller dataset (precisely the shape is just of (10815, 243)) and most of them are zero. The reason is because actually the genes carry most of information were the ones in the last quartile.

Therefore we now come back to the original situation and investigate the sparsity of the data.

¶

1.2.6) SPARSITY OF DATA

We have seen thorugh previosly charts and outliers checks that most genes which carry values are out of the 75% quartile, therefore removing the outliers is not worth. Moreover this is explained by the fact that most of data is sparse.

We now represent the dataframe as a simple matrix and look at how many zero we have. Then we compute the percentage of the sparsity on our dataset (we basically subtract to one the number_of_non_zero_genes/total_number of genes)

In [ ]:
# We proceed in the following way. We start representing the dataframe as a simple matrix to see how many zero we have 
# then we compute the percentage of the sparsity on our dataset doing 1 - number_of_non_zero_genes / total_number of genes

df_HCC1806_unf_Matrix = np.array(df_HCC1806_unf)
print("Matrix useful for the sparsity check: \n", df_HCC1806_unf_Matrix)

Matrix useful for the sparsity check: 
 [[ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]
 ...
 [26 62  0 ... 15  6 34]
 [66 71  1 ... 36  8 79]
 [ 0  4  0 ...  2  1  5]]
In [ ]:
Sparsity_matrix = 1 - count_nonzero(df_HCC1806_unf_Matrix)/ df_HCC1806_unf_Matrix.size
Sparsity_matrix
Out[ ]:
0.558456230779135

Again (as in the case of MCF7) also the dataframe of HCC1806 is highly sparse, infact the percentage of sparsity is more then 55% of the total dataframe.

¶

1.2.7) DATASET TRANSPOSITION AND APPLICATION OF STATISTICAL TOOLS

We now repeat the same steps (representation of means, std, etc) using the dataset transposed in order to see which genes are highly expressed with respect to the others

In [ ]:
df_HCC1806_unf_transposed = df_HCC1806_unf.transpose() # we transpose the dataset in order to get cells 
# as rows and genes as columns
df_HCC1806_unf_transposed
Out[ ]:
"WASH7P" "CICP27" "DDX11L17" "WASH9P" "OR4F29" "MTND1P23" "MTND2P28" "MTCO1P12" "MTCO2P12" "MTATP8P1" ... "MT-TH" "MT-TS2" "MT-TL2" "MT-ND5" "MT-ND6" "MT-TE" "MT-CYB" "MT-TT" "MT-TP" "MAFIP"
"output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" 0 0 0 0 2 250 54 1740 6 1 ... 17 5 15 3852 900 22 4208 26 66 0
"output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" 0 0 0 0 0 424 100 2340 5 1 ... 49 29 36 7457 1439 43 6491 62 71 4
"output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam" 0 0 0 0 0 11 0 22 0 0 ... 0 0 0 93 12 0 25 0 1 0
"output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" 0 0 0 0 0 63 16 1020 3 0 ... 43 17 8 1479 234 0 4819 11 3 7
"output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" 0 0 0 0 0 27 3 303 0 0 ... 0 0 3 303 33 0 310 4 9 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
"output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" 0 0 0 1 0 146 47 961 1 0 ... 25 17 11 2926 423 26 3719 42 48 3
"output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" 0 0 0 0 0 37 6 372 0 0 ... 2 1 3 688 114 1 984 1 18 0
"output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" 0 0 0 1 0 83 30 1128 4 0 ... 9 6 23 2999 486 4 2256 15 36 2
"output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" 0 0 0 0 0 47 7 258 1 0 ... 2 3 3 611 75 4 981 6 8 1
"output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam" 0 0 0 0 0 249 38 980 0 0 ... 14 3 15 2460 513 20 2039 34 79 5

243 rows × 23396 columns

In [ ]:
df_HCC1806_unf_summary2 = df_HCC1806_unf_transposed.describe()
df_HCC1806_unf_summary2
Out[ ]:
"WASH7P" "CICP27" "DDX11L17" "WASH9P" "OR4F29" "MTND1P23" "MTND2P28" "MTCO1P12" "MTCO2P12" "MTATP8P1" ... "MT-TH" "MT-TS2" "MT-TL2" "MT-ND5" "MT-ND6" "MT-TE" "MT-CYB" "MT-TT" "MT-TP" "MAFIP"
count 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 ... 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000
mean 0.045267 0.119342 0.469136 0.255144 0.127572 117.930041 28.427984 904.308642 1.403292 0.378601 ... 10.358025 5.930041 13.493827 2290.213992 386.901235 18.246914 2163.588477 20.613169 46.444444 3.897119
std 0.318195 0.594531 1.455282 0.818639 0.440910 103.038022 26.062662 654.520308 1.735003 0.747361 ... 10.910199 7.040559 11.803967 1726.423259 309.276105 54.076514 1730.393947 22.224590 47.684223 4.736193
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 46.500000 8.000000 390.500000 0.000000 0.000000 ... 2.000000 1.000000 4.000000 918.000000 138.500000 4.000000 947.500000 5.000000 14.000000 0.000000
50% 0.000000 0.000000 0.000000 0.000000 0.000000 94.000000 22.000000 790.000000 1.000000 0.000000 ... 7.000000 4.000000 10.000000 1848.000000 320.000000 11.000000 1774.000000 14.000000 38.000000 2.000000
75% 0.000000 0.000000 0.000000 0.000000 0.000000 157.000000 42.000000 1208.000000 2.000000 1.000000 ... 14.000000 7.500000 20.000000 3172.000000 528.000000 20.000000 2927.000000 30.500000 64.500000 6.000000
max 3.000000 5.000000 12.000000 6.000000 4.000000 694.000000 120.000000 3569.000000 12.000000 4.000000 ... 52.000000 43.000000 57.000000 8972.000000 1439.000000 804.000000 11383.000000 154.000000 409.000000 24.000000

8 rows × 23396 columns

Plotting the distributionn of the mean in the genes, the std, and the 75-percentile we can notice that most of genes are not present in mst of the cells.

In [ ]:
# We now plot more or less the same graph of before to see the expression of individual genes across cells 
sns.displot(df_HCC1806_unf_summary2.iloc[1], bins=600, color="green")
plt.xlim(0, 800) # we limit the x axis to 800 in order to avoid small pictures
# this is also allowed because the values of x-axis stop before
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
sns.displot(df_HCC1806_unf_summary2.iloc[2], bins=600, color="skyblue")
plt.xlim(0, 800) # we limit the x axis to 800 in order to avoid small pictures
# this is also allowed because the values of x-axis stop before
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
#75% quantile
sns.displot(df_HCC1806_unf_summary2.iloc[6], bins=600, color="purple")
plt.xlim(0, 800) # we limit the x axis to 800 in order to avoid small pictures
# this is also allowed because the values of x-axis stop before
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Also from this persepctive we can see that most genes have not value higher then zero. We can avoid to represent again the 25% and 50% quantiles

¶

1.2.8) CHECK OF NORMALIZATION OF DATA USING SKEWENESS AND KURTOSIS

Now we analyze the skewness and Kurtosis for each single cell to verify if it is normalized or not ( since most of these values are not near 0, we can deudce that most cells have a different distribution from the normal one).

In [ ]:
from scipy.stats import kurtosis, skew
cnames = list(df_HCC1806_unf.columns)
colN = np.shape(df_HCC1806_unf)[1]
colN
df_skew_cells = []
for i in range(colN) :     
     v_df = df_HCC1806_unf[cnames[i]]
     df_skew_cells += [skew(v_df)]   
df_skew_cells
sns.histplot(df_skew_cells,bins=100, color='blue', kde=True)
plt.xlabel('Skewness of single cells expression profiles - original df')
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
df_kurt_cells = []
for i in range(colN) :     
     v_df = df_HCC1806_unf[cnames[i]]
     df_kurt_cells += [kurtosis(v_df)] 
df_kurt_cells
sns.histplot(df_kurt_cells,bins=100, color='green', kde=True)
plt.xlabel('Kurtosis of single cells expression profiles - original df')
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
# Now we compute the difference between the two distributions and our distributions
xnorm = np.random.normal(0, 2, 10000000)   # create random values based on a normal distribution
xnorm 

print( "Excess kurtosis of normal distribution: ",  kurtosis(xnorm) )
print( "Skewness of normal distribution: ", skew(xnorm) )

Excess kurtosis of normal distribution:  -0.0010484252979767739
Skewness of normal distribution:  -0.001307851392970603

sss

¶

1.2.9) DATA TRANSORMATIONS USING LOG 2

We now try to transform the data, computing the log base 2 across all the dataset in order to see if this transformation can be a good normalization

In [ ]:
# First we add 1 to all the dataset to avoid log(0) and then we compute the log of the dataset
df_HCC1806_unf_log = (df_HCC1806_unf + 1).apply(np.log2)
df_HCC1806_unf_log
Out[ ]:
"output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam" ... "output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam"
"WASH7P" 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
"CICP27" 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
"DDX11L17" 0.000000 0.000000 0.00000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
"WASH9P" 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000
"OR4F29" 1.584963 0.000000 0.00000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
"MT-TE" 4.523562 5.459432 0.00000 0.000000 0.000000 2.000000 5.584963 2.321928 1.584963 3.169925 ... 4.643856 4.000000 4.000000 2.321928 2.321928 4.754888 1.000000 2.321928 2.321928 4.392317
"MT-CYB" 12.039262 12.664447 4.70044 12.234817 8.280771 9.442943 11.494856 10.600842 8.519636 10.837628 ... 10.129283 10.481799 9.659996 9.965784 9.840778 11.861087 9.943980 11.140191 9.939579 10.994353
"MT-TT" 4.754888 5.977280 0.00000 3.584963 2.321928 0.000000 5.392317 3.321928 1.584963 3.169925 ... 5.614710 5.000000 2.000000 3.169925 2.584963 5.426265 1.000000 4.000000 2.807355 5.129283
"MT-TP" 6.066089 6.169925 1.00000 2.000000 3.321928 3.906891 6.523562 4.523562 2.000000 4.954196 ... 6.906891 5.727920 3.584963 4.523562 4.000000 5.614710 4.247928 5.209453 3.169925 6.321928
"MAFIP" 0.000000 2.321928 0.00000 3.000000 0.000000 3.321928 0.000000 2.321928 1.584963 0.000000 ... 1.584963 0.000000 1.584963 1.000000 1.000000 2.000000 0.000000 1.584963 1.000000 2.584963

23396 rows × 243 columns

In [ ]:
# We now apply again the method .describe() to see how information as mean/std is changed
df_HCC1806_unf_log_summary = df_HCC1806_unf_log.describe()
df_HCC1806_unf_log_summary
Out[ ]:
"output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam" ... "output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam"
count 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 ... 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000
mean 2.665234 3.224613 1.366411 2.261582 2.109583 1.827055 2.635027 2.887775 2.028888 1.877170 ... 2.822969 2.797182 2.234019 2.030740 2.048194 3.118012 2.252949 2.805019 2.163035 2.764438
std 3.341225 3.853466 1.938462 3.620534 2.760889 2.810320 3.651689 3.546553 2.807174 2.831652 ... 3.245582 3.438419 2.969904 2.611961 2.648009 3.413622 2.758163 3.443798 2.781778 3.167457
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 1.000000 0.000000 0.000000 0.000000 0.000000 1.584963 0.000000 0.000000 0.000000 1.000000
75% 5.700440 6.977280 2.584963 5.357552 4.523562 4.169925 6.357552 6.266787 4.523562 4.247928 ... 5.832890 6.087463 4.906891 4.247928 4.321928 6.266787 4.643856 6.108524 4.523562 5.491853
max 15.114637 16.075751 12.632995 16.099327 14.080734 15.533908 15.394798 15.938799 14.884218 15.113539 ... 14.260773 14.919422 14.418314 13.716176 13.487966 15.030279 13.911017 15.073430 13.414949 14.800344

8 rows × 243 columns

In [ ]:
# we now plot more or less some charts (at least mean, std and 75% quartile)
sns.displot(data = df_HCC1806_unf_log_summary.iloc[1], bins=50, color="green")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
sns.displot(data = df_HCC1806_unf_log_summary.iloc[2], bins=50, color="skyblue")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
sns.displot(data = df_HCC1806_unf_log_summary.iloc[6], bins=50, color="red")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

What we notice is that we have less zero and smaller ranges compared to the original situation

In [ ]:
# We apply again skeweness and kurtosis to the log transformed dataset
from scipy.stats import kurtosis, skew
cnames = list(df_HCC1806_unf_log.columns)
colN = np.shape(df_HCC1806_unf_log)[1]
colN
df_skew_cells = []
for i in range(colN) :     
     v_df = df_HCC1806_unf_log[cnames[i]]
     df_skew_cells += [skew(v_df)]   
df_skew_cells
sns.histplot(df_skew_cells,bins=100, color='blue', kde=True)
plt.xlabel('Skewness of single cells expression profiles - original df')
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
df_kurt_cells = []
for i in range(colN) :     
     v_df = df_HCC1806_unf_log[cnames[i]]
     df_kurt_cells += [kurtosis(v_df)]   
df_kurt_cells
sns.histplot(df_kurt_cells,bins=100, color='green',  kde=True)
plt.xlabel('Kurtosis of single cells expression profiles - original df')
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

As final observation we can notice that most data has value zero in both skeweness and kurtosis telling us that there is no asymmetry and the data is more normalized.

¶

  1. Analysis on unfiltered/filtered Smartseq and Unsupervided/Supervised learning on SmartSeq

¶

2.1) MCF7

¶

2.1.0) Importing datasets

In [ ]:
df_MCF7_unf = pd.read_csv("SmartSeq\MCF7_SmartS_Unfiltered_Data.txt",delimiter="\ ", engine='python',index_col=0)
print("UNFILTERED dimensions:", np.shape(df_MCF7_unf))
df_MCF7_unf_summary=df_MCF7_unf.describe()
df_MCF7_unf.describe()

UNFILTERED dimensions: (22934, 383)
Out[ ]:
"output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" "output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" "output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" "output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam" "output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam" "output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam" "output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam" "output.STAR.1_A7_Hypo_S25_Aligned.sortedByCoord.out.bam" ... "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" "output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam" "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam"
count 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 ... 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000
mean 40.817651 0.012253 86.442400 1.024636 14.531351 56.213613 75.397183 62.767725 67.396747 2.240734 ... 17.362562 42.080230 34.692422 32.735284 21.992718 17.439391 49.242784 61.545609 68.289352 62.851400
std 465.709940 0.207726 1036.572689 6.097362 123.800530 503.599145 430.471519 520.167576 459.689019 25.449630 ... 193.153757 256.775704 679.960908 300.291051 153.441647 198.179666 359.337479 540.847355 636.892085 785.670341
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
75% 17.000000 0.000000 5.000000 0.000000 7.000000 23.000000 39.000000 35.000000 38.000000 1.000000 ... 9.000000 30.000000 0.000000 17.000000 12.000000 9.000000 27.000000 30.000000 38.000000 33.000000
max 46744.000000 14.000000 82047.000000 289.000000 10582.000000 46856.000000 29534.000000 50972.000000 36236.000000 1707.000000 ... 17800.000000 23355.000000 81952.000000 29540.000000 12149.000000 19285.000000 28021.000000 40708.000000 46261.000000 68790.000000

8 rows × 383 columns

In [ ]:
mcf_filtered = pd.read_csv("SmartSeq\MCF7_SmartS_Filtered_Data.txt",delimiter="\ ", engine='python',index_col=0)
print("FILTERED dimensions:", np.shape(mcf_filtered))
mcf_filtered_summary=mcf_filtered.describe()
mcf_filtered.describe()

FILTERED dimensions: (18945, 313)
Out[ ]:
"output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" "output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam" "output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam" "output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam" "output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam" "output.STAR.1_B11_Hypo_S77_Aligned.sortedByCoord.out.bam" "output.STAR.1_B12_Hypo_S78_Aligned.sortedByCoord.out.bam" "output.STAR.1_B4_Norm_S52_Aligned.sortedByCoord.out.bam" ... "output.STAR.4_H10_Hypo_S382_Aligned.sortedByCoord.out.bam" "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam"
count 18945.000000 18945.000000 18945.000000 18945.000000 18945.000000 18945.000000 18945.000000 18945.000000 18945.000000 18945.000000 ... 18945.000000 18945.000000 18945.000000 18945.000000 18945.000000 18945.000000 18945.000000 18945.000000 18945.000000 18945.000000
mean 49.409290 104.620639 17.589707 68.045395 91.260333 75.979784 81.576194 85.303985 49.655529 16.382792 ... 54.711375 21.016785 50.920137 39.622486 26.620164 21.099023 59.585537 74.487305 82.655054 76.081499
std 511.986757 1139.662971 136.014975 553.362211 472.099720 571.441098 504.632248 911.153373 406.561440 160.981562 ... 633.970615 212.338278 281.722199 329.984580 168.460343 217.871697 394.584632 594.260858 699.898130 863.857880
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 1.000000 1.000000 4.000000 6.000000 4.000000 1.000000 3.000000 0.000000 ... 0.000000 1.000000 4.000000 1.000000 0.000000 1.000000 0.000000 0.000000 1.000000 1.000000
75% 28.000000 27.000000 10.000000 36.000000 57.000000 52.000000 55.000000 48.000000 33.000000 9.000000 ... 28.000000 13.000000 42.000000 25.000000 17.000000 13.000000 42.000000 45.000000 55.000000 48.000000
max 46744.000000 82047.000000 10582.000000 46856.000000 29534.000000 50972.000000 36236.000000 56068.000000 24994.000000 13587.000000 ... 49147.000000 17800.000000 23355.000000 29540.000000 12149.000000 19285.000000 28021.000000 40708.000000 46261.000000 68790.000000

8 rows × 313 columns

In [ ]:
mcf_removed_elements = df_MCF7_unf[~df_MCF7_unf.index.isin(mcf_filtered.index)]
columns_to_remove = mcf_filtered.columns.intersection(mcf_removed_elements.columns)
mcf_removed_elements = df_MCF7_unf.drop(columns=columns_to_remove)
mcf_removed_elements_summary=mcf_removed_elements.describe()
print("REMOVED DATA dimensions:", np.shape(mcf_removed_elements))
mcf_removed_elements.describe()

REMOVED DATA dimensions: (22934, 70)
Out[ ]:
"output.STAR.1_A11_Hypo_S29_Aligned.sortedByCoord.out.bam" "output.STAR.1_A1_Norm_S1_Aligned.sortedByCoord.out.bam" "output.STAR.1_A7_Hypo_S25_Aligned.sortedByCoord.out.bam" "output.STAR.1_A8_Hypo_S26_Aligned.sortedByCoord.out.bam" "output.STAR.1_A9_Hypo_S27_Aligned.sortedByCoord.out.bam" "output.STAR.1_B10_Hypo_S76_Aligned.sortedByCoord.out.bam" "output.STAR.1_B1_Norm_S49_Aligned.sortedByCoord.out.bam" "output.STAR.1_B2_Norm_S50_Aligned.sortedByCoord.out.bam" "output.STAR.1_B3_Norm_S51_Aligned.sortedByCoord.out.bam" "output.STAR.1_B9_Hypo_S75_Aligned.sortedByCoord.out.bam" ... "output.STAR.3_E10_Hypo_S232_Aligned.sortedByCoord.out.bam" "output.STAR.3_E3_Norm_S207_Aligned.sortedByCoord.out.bam" "output.STAR.3_E7_Hypo_S229_Aligned.sortedByCoord.out.bam" "output.STAR.3_F11_Hypo_S281_Aligned.sortedByCoord.out.bam" "output.STAR.3_G3_Norm_S303_Aligned.sortedByCoord.out.bam" "output.STAR.3_H7_Hypo_S373_Aligned.sortedByCoord.out.bam" "output.STAR.4_A12_Hypo_S48_Aligned.sortedByCoord.out.bam" "output.STAR.4_A3_Norm_S21_Aligned.sortedByCoord.out.bam" "output.STAR.4_A5_Norm_S23_Aligned.sortedByCoord.out.bam" "output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam"
count 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 ... 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000
mean 0.012253 1.024636 2.240734 0.989971 3.810325 99.728133 5.728046 5.588689 9.081146 0.005625 ... 0.006671 0.018401 0.001308 25.948286 35.644981 0.389858 3.063966 56.798291 0.286561 34.692422
std 0.207726 6.097362 25.449630 14.975347 57.618692 1035.207158 52.387257 45.051444 43.536175 0.289519 ... 0.181321 1.417297 0.039599 260.431089 998.534500 3.654229 23.088568 463.501312 0.928592 679.960908
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
75% 0.000000 0.000000 1.000000 0.000000 1.000000 43.000000 3.000000 3.000000 4.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 32.000000 0.000000 0.000000
max 14.000000 289.000000 1707.000000 1267.000000 3816.000000 60475.000000 5960.000000 3631.000000 2418.000000 36.000000 ... 20.000000 180.000000 2.000000 21842.000000 142393.000000 251.000000 1637.000000 34790.000000 45.000000 81952.000000

8 rows × 70 columns

¶

2.1.1) Statistical study on mean and variances on cells

In [ ]:
df_MCF7_unf_summary = df_MCF7_unf.describe()
¶

2.1.1.1) Study of mean on cells

We start studying the distribution of the means in the unfiltered dataset.

In particular, after watching the minimum and maximum value of them we study the histogram.

In [ ]:
df_MCF7_unf_mean_max_value = df_MCF7_unf_summary.iloc[1].max()
print("Max value in unfiltered data:", df_MCF7_unf_mean_max_value)
df_MCF7_unf_mean_min_value = df_MCF7_unf_summary.iloc[1].min()
print("Min value in unfiltered data:", df_MCF7_unf_mean_min_value)

Max value in unfiltered data: 100.639094793756
Min value in unfiltered data: 4.360338362256911e-05
In [ ]:
sns.displot(df_MCF7_unf_summary.iloc[1], bins=100)
plt.xlim(0, 105) 
plt.ylim(0, 30)
plt.title("Distribution of mean in unfiltered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

In unfiltered dataset means are mostly concentrated around in interval [0, 10] and on interval [20, 80] with some peaks around 30, 50 and 70.

Then we move over to filtered dataset and we apply same staregy done before

In [ ]:
mcf_filtered_mean_max_value = mcf_filtered_summary.iloc[1].max()
print("Max value in filtered data:", mcf_filtered_mean_max_value)
mcf_filtered_mean_min_value = mcf_filtered_summary.iloc[1].min()
print("Min value in filtered data:", mcf_filtered_mean_min_value)

Max value in filtered data: 104.62063869094747
Min value in filtered data: 13.900395882818685
In [ ]:
sns.displot(mcf_filtered_summary.iloc[1], bins=50)
plt.xlim(0, 105)
plt.ylim(0,20)
plt.title("Distribution of mean in filtered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

This time the lowest mean is 13.9 where there is the first peak. Then the distribution is non-zero on the entire interval. Focusing it is more dense in interval [40, 90] with some peaks on 40, 60 and 80

Finally, we move over to the dataset made of elements that are in unfiltered dataset but not in filtered (removed elements)

In [ ]:
mcf_removed_elements_mean_max_value = mcf_removed_elements_summary.iloc[1].max()
print("Max value in removed elements:", mcf_removed_elements_mean_max_value)
mcf_removed_elements_mean_min_value = mcf_removed_elements_summary.iloc[1].min()
print("Min value in removed elements:", mcf_removed_elements_mean_min_value)

Max value in removed elements: 100.639094793756
Min value in removed elements: 4.360338362256911e-05
In [ ]:
sns.displot(mcf_removed_elements_summary.iloc[1], bins=50)
plt.xlim(0, 105)
plt.ylim(0,50)
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Vast majority of removed elements has low mean, between 0 and 10

Going over we checked if elements with highest and lowest mean in unfiltered and filtered datasets were the same.

In case of negative answere, we checked if the element with highest or lowest mean in unfiltered are contained in filtered.

Outputs are as follow.

In [ ]:
# is cell with highest mean value in filtered and unfiltered data the same?

max_mean_column_filtered = mcf_filtered.mean().idxmax()
max_mean_column_unfiltered = df_MCF7_unf.mean().idxmax()

print("is cell with highest mean value in filtered and unfiltered data the same?", max_mean_column_filtered==max_mean_column_unfiltered,
      "\nmax mean column in filtered data:", max_mean_column_filtered, " and the value is:", mcf_filtered.mean().max(),
      "\nmax mean column in unfiltered data:", max_mean_column_unfiltered, " and the value is:", df_MCF7_unf.mean().max()) 

is cell with highest mean value in filtered and unfiltered data the same? False 
max mean column in filtered data: "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam"  and the value is: 104.62063869094747 
max mean column in unfiltered data: "output.STAR.2_C7_Hypo_S127_Aligned.sortedByCoord.out.bam"  and the value is: 100.639094793756
In [ ]:
# is cell with highest mean value in unfiltered contained in filtered?

if max_mean_column_unfiltered in mcf_filtered:
    print("yes")
else:
    print("no")

no
In [ ]:
# is cell with lowest mean value in filtered and unfiltered data the same?

min_mean_column_filtered = mcf_filtered.mean().idxmin()
min_mean_column_unfiltered = df_MCF7_unf.mean().idxmin()

print("is cell with lowest mean value in filtered and unfiltered data the same?", min_mean_column_filtered==min_mean_column_unfiltered,
        "\nmin mean column in filtered data:", min_mean_column_filtered, " and the value is:", mcf_filtered.mean().min(),
        "\nmin mean column in unfiltered data:", min_mean_column_unfiltered, " and the value is:", df_MCF7_unf.mean().min())

is cell with lowest mean value in filtered and unfiltered data the same? False 
min mean column in filtered data: "output.STAR.1_E2_Norm_S194_Aligned.sortedByCoord.out.bam"  and the value is: 13.900395882818685 
min mean column in unfiltered data: "output.STAR.1_H1_Norm_S337_Aligned.sortedByCoord.out.bam"  and the value is: 4.360338362256911e-05
In [ ]:
# is cell with lowest mean value in unfiltered contained in filtered?

if min_mean_column_unfiltered in mcf_filtered:
    print("yes")
else:
    print("no")

no
¶

2.1.1.2) Study of STD

Now we apply same strategy done before (2.1.1.1) on STD

In [ ]:
df_MCF7_unf_std_max_value = df_MCF7_unf_summary.iloc[2].max()
print("max STD value in unfiltered:", df_MCF7_unf_std_max_value)
df_MCF7_unf_std_min_value = df_MCF7_unf_summary.iloc[2].min()
print("min TD value in unfiltered:", df_MCF7_unf_std_min_value)

max STD value in unfiltered: 1668.9422310403038
min TD value in unfiltered: 0.006603285820148005
In [ ]:
sns.displot(df_MCF7_unf_summary.iloc[2], bins=100)
plt.xlim(0, 1750)
plt.ylim(0,50)
plt.title("Distribution of STD in unfiltered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

STD in unfiltered dataset are mainly distributed in the interval [0, 1000] with peaks on 0, 250, 400, 600 and 1000

In [ ]:
mcf_filtered_std_max_value = mcf_filtered_summary.iloc[2].max()
print("max STD value in filtered:", mcf_filtered_std_max_value)
mcf_filtered_std_min_value = mcf_filtered_summary.iloc[2].min()
print("min STD value in filtered:", df_MCF7_unf_std_min_value)

max STD value in filtered: 1835.9683350446378
min STD value in filtered: 0.006603285820148005
In [ ]:
sns.displot(mcf_filtered_summary.iloc[2], bins=100)
plt.xlim(0, 2000)
plt.ylim(0,25)
plt.title("Distribution of STD in filtered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

In filtered dataset STD are distributed mainly on interval [50, 700], with peaks on 300, 400 and 600; moreover there is a sligh distribution on (700, 1200]

In [ ]:
mcf_removed_elements_std_max_value = mcf_removed_elements_summary.iloc[2].max()
print("max STD value in removed elements:", mcf_removed_elements_std_max_value)
mcf_removed_elements_std_min_value = mcf_removed_elements_summary.iloc[2].min()
print("min STD value in removed elements:", mcf_removed_elements_std_min_value)

max STD value in removed elements: 1563.2626742257273
min STD value in removed elements: 0.006603285820148005
In [ ]:
sns.displot(mcf_removed_elements_summary.iloc[2], bins=100)
plt.xlim(0, 1750)
plt.ylim(0,50)
plt.title("Distribution of STD in removed elements")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Also this time in removed elements, STD is concentrated mostly around 0

In [ ]:
# is cell with highest std value in filtered and unfiltered data the same?

max_std_column_filtered = mcf_filtered.std().idxmax()
max_std_column_unfiltered = df_MCF7_unf.std().idxmax()

print("is cell with max std value in filtered and unfiltered data the same?", max_std_column_filtered==max_std_column_unfiltered,
        "\nmin std column in filtered data:", max_std_column_filtered, " and the value is:", mcf_filtered.std().max(),
        "\nmin std column in unfiltered data:", max_std_column_unfiltered, " and the value is:", df_MCF7_unf.std().max())

is cell with max std value in filtered and unfiltered data the same? True 
min std column in filtered data: "output.STAR.1_H12_Hypo_S366_Aligned.sortedByCoord.out.bam"  and the value is: 1835.968335044638 
min std column in unfiltered data: "output.STAR.1_H12_Hypo_S366_Aligned.sortedByCoord.out.bam"  and the value is: 1668.9422310403038
In [ ]:
# is cell with max std value in unfiltered contained in filtered?

if max_std_column_unfiltered in mcf_filtered:
    print("yes")
else:
    print("no")

yes
In [ ]:
# is cell with lowest std value in filtered and unfiltered data the same?

min_std_column_filtered = mcf_filtered.std().idxmin()
min_std_column_unfiltered = df_MCF7_unf.std().idxmin()

print("is cell with lowest std value in filtered and unfiltered data the same?", min_std_column_filtered==min_std_column_unfiltered,
        "\nmin std column in filtered data:", min_std_column_filtered, " and the value is:", mcf_filtered.std().min(),
        "\nmin std column in unfiltered data:", min_std_column_unfiltered, " and the value is:", df_MCF7_unf.std().min())

is cell with lowest std value in filtered and unfiltered data the same? False 
min std column in filtered data: "output.STAR.1_C1_Norm_S97_Aligned.sortedByCoord.out.bam"  and the value is: 72.76143736279771 
min std column in unfiltered data: "output.STAR.1_H1_Norm_S337_Aligned.sortedByCoord.out.bam"  and the value is: 0.006603285820148005
In [ ]:
# is cell with lowest std value in unfiltered contained in filtered?

if min_std_column_unfiltered in mcf_filtered:
    print("yes")
else:
    print("no")

no

¶

2.1.1.3) DETAILS OF DISTRIBUTION OF MEANS

Continuing the study done before, we continue analizing the distribution of mean with the function describe()

In [ ]:
df_MCF7_unf_summary.iloc[1].describe()
Out[ ]:
count    383.000000
mean      43.368444
std       23.998135
min        0.000044
25%       26.107548
50%       49.242784
75%       61.421405
max      100.639095
Name: mean, dtype: float64

In unfiltered dataset mean of means is 43.38, slightly lower than the median (49.24), suggesting that the symmetry of distribution is shifted toward left.

Given minimum equal to 0 and maximum equal to 100, the expected 25%-quantile, 50%-quantile and 75%-quantile in case of uniform distribution would have been 25, 50 and 75 while in this case are 25, 49 and 61, suggesting that many elements are concentrated in the interval of lenght between 50 and 60 and few of them in the interval of lenght 40 between 60 and 100

In [ ]:
mcf_filtered_summary.iloc[1].describe()
Out[ ]:
count    313.000000
mean      61.114583
std       20.920048
min       13.900396
25%       46.456638
50%       63.289047
75%       77.084930
max      104.620639
Name: mean, dtype: float64

In filtered dataset the median is closer to the mean than what happens in unfiltered, implying that the distribution should be more symmetrical.

Given minimum value equal to 14 and maximum equal to 104 the expected 25%-quantile,50%-quantile and 75%-quantile in case of uniform distribution would have been 36.5, 65 and 89, while in this case are 46, 63 and 77, suggesting that implying that 50% of elements is in the 31 length interval [46,77] and the other 50% in the 59 length interval [14,46]U[77,104].

Furthermore, STD from unfiltered to filtered decreases, implying that the distribution in filtered is less spread than before.

In [ ]:
mcf_removed_elements_summary.iloc[1].describe()
Out[ ]:
count     70.000000
mean      11.505886
std       24.024986
min        0.000044
25%        0.264237
50%        3.132489
75%        6.413818
max      100.639095
Name: mean, dtype: float64

In removed elememts all statistics confirm what said before that majority of elements have low mean.

¶

2.1.1.4) DETAILS OF DISTRIBUTION OF STD

We apply same strategy as before (2.2.3) on STD

In [ ]:
df_MCF7_unf_summary.iloc[2].describe()
Out[ ]:
count     383.000000
mean      420.606147
std       287.762585
min         0.006603
25%       230.616936
50%       398.218463
75%       570.957846
max      1668.942231
Name: std, dtype: float64
In [ ]:
mcf_filtered_summary.iloc[2].describe()
Out[ ]:
count     313.000000
mean      533.541022
std       267.786954
min        72.761437
25%       355.653903
50%       483.889437
75%       646.890817
max      1835.968335
Name: std, dtype: float64

From unfiltered to filtered, the mean STD increases from 420 to 533 implying that datas are more spread.

In [ ]:
mcf_removed_elements_summary.iloc[2].describe()
Out[ ]:
count      70.000000
mean      130.276923
std       293.139183
min         0.006603
25%         1.890654
50%        26.661737
75%        54.779254
max      1563.262674
Name: std, dtype: float64

¶

2.1.2) STUDY ON genes by mean and variances on genes

We procede to do same studies done before (see 2.1.1)

In [ ]:
df_MCF7_unf_transposed = df_MCF7_unf.T
print("UNFILTERED TRANSPOSED dimensions:", np.shape(df_MCF7_unf_transposed))
df_MCF7_unf_transposed_summary=df_MCF7_unf_transposed.describe()

UNFILTERED TRANSPOSED dimensions: (383, 22934)
In [ ]:
mcf_filtered_transposed = mcf_filtered.T
print("FILTERED TRANSPOSED dimensions:", np.shape(mcf_filtered_transposed))
mcf_filtered_transposed_summary=mcf_filtered_transposed.describe()
mcf_filtered_transposed.describe()

FILTERED TRANSPOSED dimensions: (313, 18945)
Out[ ]:
"WASH7P" "MIR6859-1" "WASH9P" "OR4F29" "MTND1P23" "MTND2P28" "MTCO1P12" "MTCO2P12" "MTATP8P1" "MTATP6P1" ... "MT-TH" "MT-TS2" "MT-TL2" "MT-ND5" "MT-ND6" "MT-TE" "MT-CYB" "MT-TT" "MT-TP" "MAFIP"
count 313.000000 313.000000 313.000000 313.000000 313.000000 313.000000 313.000000 313.000000 313.000000 313.000000 ... 313.000000 313.000000 313.000000 313.000000 313.000000 313.000000 313.000000 313.000000 313.000000 313.000000
mean 0.156550 0.031949 1.587859 0.054313 0.057508 7.067093 5.300319 0.607029 0.083067 251.246006 ... 1.194888 0.517572 3.642173 1157.523962 231.782748 5.734824 2723.453674 2.463259 6.418530 2.012780
std 0.673036 0.275498 2.358722 0.299982 0.246546 7.083203 5.792283 1.026359 0.309260 244.053050 ... 2.046817 1.168707 4.307835 899.677268 196.936662 6.539571 2862.158823 3.532735 7.170133 3.867165
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000 0.000000 0.000000 48.000000 ... 0.000000 0.000000 0.000000 429.000000 90.000000 1.000000 421.000000 0.000000 1.000000 0.000000
50% 0.000000 0.000000 1.000000 0.000000 0.000000 5.000000 3.000000 0.000000 0.000000 178.000000 ... 0.000000 0.000000 2.000000 1027.000000 191.000000 4.000000 1360.000000 1.000000 4.000000 0.000000
75% 0.000000 0.000000 2.000000 0.000000 0.000000 12.000000 9.000000 1.000000 0.000000 408.000000 ... 2.000000 1.000000 6.000000 1667.000000 325.000000 9.000000 4615.000000 4.000000 9.000000 3.000000
max 9.000000 4.000000 20.000000 3.000000 2.000000 41.000000 31.000000 6.000000 2.000000 1491.000000 ... 15.000000 8.000000 20.000000 4835.000000 1334.000000 40.000000 16026.000000 22.000000 42.000000 30.000000

8 rows × 18945 columns

In [ ]:
mcf_removed_elements_transposed=mcf_removed_elements.T
print("REMOVED DATA dimensions:", np.shape(mcf_removed_elements_transposed))
mcf_removed_elements_transposed_summary=mcf_removed_elements_transposed.describe()
mcf_removed_elements_transposed.describe()

REMOVED DATA dimensions: (70, 22934)
Out[ ]:
"WASH7P" "MIR6859-1" "WASH9P" "OR4F29" "MTND1P23" "MTND2P28" "MTCO1P12" "MTCO2P12" "MTATP8P1" "MTATP6P1" ... "MT-TH" "MT-TS2" "MT-TL2" "MT-ND5" "MT-ND6" "MT-TE" "MT-CYB" "MT-TT" "MT-TP" "MAFIP"
count 70.000000 70.0 70.000000 70.000000 70.000000 70.000000 70.000000 70.000000 70.000000 70.000000 ... 70.000000 70.000000 70.000000 70.000000 70.000000 70.000000 70.000000 70.000000 70.000000 70.000000
mean 0.028571 0.0 0.257143 0.057143 0.014286 2.657143 1.914286 0.157143 0.028571 91.528571 ... 0.457143 0.114286 0.928571 385.800000 83.057143 1.985714 816.771429 0.385714 2.085714 0.571429
std 0.239046 0.0 1.125279 0.376318 0.119523 8.597077 7.338094 0.628680 0.239046 302.238255 ... 1.594244 0.525932 3.271845 1219.654758 275.956145 6.275078 2675.968573 1.721921 8.019438 3.828086
min 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000
50% 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 4.000000 ... 0.000000 0.000000 0.000000 32.000000 5.000000 0.000000 40.000000 0.000000 0.000000 0.000000
75% 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 20.000000 ... 0.000000 0.000000 0.000000 88.500000 14.000000 1.000000 235.500000 0.000000 0.000000 0.000000
max 2.000000 0.0 7.000000 3.000000 1.000000 45.000000 36.000000 4.000000 2.000000 1662.000000 ... 10.000000 4.000000 22.000000 8115.000000 2002.000000 46.000000 15766.000000 13.000000 56.000000 32.000000

8 rows × 22934 columns

¶

2.1.2.1) Mean

In [ ]:
df_MCF7_unf_transposed_mean_max_value = df_MCF7_unf_transposed_summary.iloc[1].max()
print("Max mean value in unfiltered transposed data:", df_MCF7_unf_transposed_mean_max_value)
df_MCF7_unf_transposed_mean_min_value = df_MCF7_unf_transposed_summary.iloc[1].min()
print("Min mean value in unfiltered transposed data:", df_MCF7_unf_transposed_mean_min_value)

Max mean value in unfiltered transposed data: 23052.762402088774
Min mean value in unfiltered transposed data: 0.005221932114882507
In [ ]:
sns.displot(df_MCF7_unf_transposed_summary.iloc[1], bins=500)
plt.xlim(0, 27000)
plt.ylim(0, 1000)
plt.title("Distribution of mean in unfiltered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Histogram of distribution of means in genes in unfiltered elemements say that almost all elements have mean close to 0

In [ ]:
mcf_filtered_transposed_mean_max_value = mcf_filtered_transposed_summary.iloc[1].max()
print("Max mean value in filtered transposed data:", mcf_filtered_transposed_mean_max_value)
mcf_filtered_transposed_mean_min_value = mcf_filtered_transposed_summary.iloc[1].min()
print("Min mean value in filtered transposed data:", mcf_filtered_transposed_mean_min_value)

Max mean value in filtered transposed data: 26918.849840255592
Min mean value in filtered transposed data: 0.019169329073482427
In [ ]:
sns.displot(mcf_filtered_transposed_summary.iloc[1], bins=500)
plt.xlim(0, 27000)
plt.ylim(0, 1000)
plt.title("Distribution of mean in filtered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Histogram of distribution of means in genes in filtered elemements say that almost all elements have mean close to 0

In [ ]:
mcf_removed_elements_transposed_mean_max_value = mcf_removed_elements_transposed_summary.iloc[1].max()
print("Max value in removed elements transposed data:", mcf_removed_elements_transposed_mean_max_value)
mcf_removed_elements_transposed_mean_min_value = mcf_removed_elements_transposed_summary.iloc[1].min()
print("Min value in removed elements transposed data:", mcf_removed_elements_transposed_mean_min_value)

Max value in removed elements transposed data: 6087.428571428572
Min value in removed elements transposed data: 0.0
In [ ]:
sns.displot(mcf_removed_elements_transposed_summary.iloc[1], bins=500)
plt.xlim(0, 6100)
plt.ylim(0, 1000)
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Histogram of distribution of means in genes in removed elemements say that almost all elements have mean close to 0

In [ ]:
# is cell with max mean value in filtered_tranposed and unfiltered_transposed data the same?

max_mean_column_filtered_transposed = mcf_filtered_transposed.mean().idxmin()
max_mean_column_unfiltered_transposed = df_MCF7_unf_transposed.mean().idxmin()

print("is cell with max mean value in filtered transposed and unfiltered transposed data the same?", max_mean_column_filtered_transposed==max_mean_column_unfiltered_transposed,
        "\nmax mean column in filtered data:", max_mean_column_filtered_transposed, " and the value is:", mcf_filtered_transposed.mean().max(),
        "\nmax mean column in unfiltered data:", max_mean_column_unfiltered_transposed, " and the value is:", df_MCF7_unf_transposed.mean().max())

is cell with max mean value in filtered transposed and unfiltered transposed data the same? False 
max mean column in filtered data: "MIR6726"  and the value is: 26918.849840255592 
max mean column in unfiltered data: "CICP3"  and the value is: 23052.762402088774
In [ ]:
# is cell with highest mean value in unfiltered contained in filtered?

if max_mean_column_unfiltered_transposed in mcf_filtered_transposed:
    print("yes")
else:
    print("no")

no
In [ ]:
# is cell with lowest mean value in filtered_tranposed and unfiltered_transposed data the same?

min_mean_column_filtered_transposed = mcf_filtered_transposed.mean().idxmin()
min_mean_column_unfiltered_transposed = df_MCF7_unf_transposed.mean().idxmin()

print("is cell with lowest mean value in filtered transposed and unfiltered transposed data the same?", min_mean_column_filtered_transposed==min_mean_column_unfiltered_transposed,
        "\nmin mean column in filtered data:", min_mean_column_filtered_transposed, " and the value is:", mcf_filtered_transposed.mean().min(),
        "\nmin mean column in unfiltered data:", min_mean_column_unfiltered_transposed, " and the value is:", df_MCF7_unf_transposed.mean().min())

is cell with lowest mean value in filtered transposed and unfiltered transposed data the same? False 
min mean column in filtered data: "MIR6726"  and the value is: 0.019169329073482427 
min mean column in unfiltered data: "CICP3"  and the value is: 0.005221932114882507
In [ ]:
# is cell with lowest mean value in unfiltered contained in filtered?

if min_mean_column_unfiltered_transposed in mcf_filtered_transposed:
    print("yes")
else:
    print("no")

no
¶

2.1.2.2) STD

In [ ]:
df_MCF7_unf_transposed_std_max_value = df_MCF7_unf_transposed_summary.iloc[2].max()
print("Max std value in unfiltered transposed data:", df_MCF7_unf_transposed_std_max_value)
df_MCF7_unf_transposed_std_min_value = df_MCF7_unf_transposed_summary.iloc[2].min()
print("Min std value in unfiltered transposed data:", df_MCF7_unf_transposed_std_min_value)

Max std value in unfiltered transposed data: 27101.548019606067
Min std value in unfiltered transposed data: 0.07216829037412606
In [ ]:
sns.displot(df_MCF7_unf_transposed_summary.iloc[2], bins=500)
plt.xlim(0, 27200)
plt.ylim(0, 1000)
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Histogram of distribution of STD in genes in unfiltered elemements say that almost all elements have mean close to 0

In [ ]:
mcf_filtered_transposed_std_max_value = mcf_filtered_transposed_summary.iloc[2].max()
print("Max std value in filtered transposed data:", mcf_filtered_transposed_mean_max_value)
mcf_filtered_transposed_std_min_value = mcf_filtered_transposed_summary.iloc[2].min()
print("Min std value in filtered transposed data:", mcf_filtered_transposed_mean_min_value)

Max std value in filtered transposed data: 26918.849840255592
Min std value in filtered transposed data: 0.019169329073482427
In [ ]:
sns.displot(mcf_filtered_transposed_summary.iloc[2], bins=1000)
plt.xlim(0, 27000)
plt.ylim(0, 1000)
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Histogram of distribution of STD in genes in filtered elemements say that almost all elements have mean close to 0

In [ ]:
mcf_removed_elements_transposed_std_max_value = mcf_removed_elements_transposed_summary.iloc[2].max()
print("Max value std in removed elements transposed data:", mcf_removed_elements_transposed_mean_max_value)
mcf_removed_elements_transposed_mean_min_value = mcf_removed_elements_transposed_summary.iloc[2].min()
print("Min value std in removed elements transposed data:", mcf_removed_elements_transposed_mean_min_value)

Max value std in removed elements transposed data: 6087.428571428572
Min value std in removed elements transposed data: 0.0
In [ ]:
sns.displot(mcf_removed_elements_transposed_summary.iloc[2], bins=1000)
plt.xlim(0, 6100)
plt.ylim(0, 3000)
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Histogram of distribution of STD in genes in removed elemements say that almost all elements have mean close to 0

In [ ]:
# is cell with max mean value in filtered_tranposed and unfiltered_transposed data the same?

max_std_column_filtered_transposed = mcf_filtered_transposed.std().idxmin()
max_std_column_unfiltered_transposed = df_MCF7_unf_transposed.std().idxmin()

print("is cell with max std value in filtered transposed and unfiltered transposed data the same?", max_std_column_filtered_transposed==max_std_column_unfiltered_transposed,
        "\nmax std column in filtered data:", max_std_column_filtered_transposed, " and the value is:", mcf_filtered_transposed.std().max(),
        "\nmax std column in unfiltered data:", max_std_column_unfiltered_transposed, " and the value is:", df_MCF7_unf_transposed.std().max())

is cell with max std value in filtered transposed and unfiltered transposed data the same? False 
max std column in filtered data: "SDHCP2"  and the value is: 27449.497138097668 
max std column in unfiltered data: "ROR1-AS1"  and the value is: 27101.54801960607
In [ ]:
# is cell with highest mean value in unfiltered contained in filtered?

if max_std_column_unfiltered_transposed in mcf_filtered_transposed:
    print("yes")
else:
    print("no")

no
In [ ]:
# is cell with lowest std value in filtered_tranposed and unfiltered_transposed data the same?

min_std_column_filtered_transposed = mcf_filtered_transposed.std().idxmin()
min_std_column_unfiltered_transposed = df_MCF7_unf_transposed.std().idxmin()

print("is cell with lowest std value in filtered transposed and unfiltered transposed data the same?", min_std_column_filtered_transposed==min_std_column_unfiltered_transposed,
        "\nmin std column in filtered data:", min_std_column_filtered_transposed, " and the value is:", mcf_filtered_transposed.std().min(),
        "\nmin std column in unfiltered data:", min_std_column_unfiltered_transposed, " and the value is:", df_MCF7_unf_transposed.std().min())

is cell with lowest std value in filtered transposed and unfiltered transposed data the same? False 
min std column in filtered data: "SDHCP2"  and the value is: 0.13733946369142572 
min std column in unfiltered data: "ROR1-AS1"  and the value is: 0.07216829037412606
In [ ]:
# is cell with lowest mean value in unfiltered contained in filtered?

if min_std_column_unfiltered_transposed in mcf_filtered_transposed:
    print("yes")
else:
    print("no")

no
¶

2.1.2.3) Details on distribution of mean

In [ ]:
df_MCF7_unf_transposed_summary.iloc[1].describe()
Out[ ]:
count    22934.000000
mean        43.368444
std        352.550337
min          0.005222
25%          0.133159
50%          3.755875
75%         29.183420
max      23052.762402
Name: mean, dtype: float64

All statistics confirm what seen before in the histogram: almost all elements are concentrated around 0.

In [ ]:
mcf_filtered_transposed_summary.iloc[1].describe()
Out[ ]:
count    18945.000000
mean        61.114583
std        451.021087
min          0.019169
25%          0.875399
50%         10.297125
75%         45.626198
max      26918.849840
Name: mean, dtype: float64

All statistics confirm what seen before in the histogram: almost all elements are concentrated around 0.

In [ ]:
mcf_removed_elements_transposed_summary.iloc[1].describe()
Out[ ]:
count    22934.000000
mean        11.505886
std         97.402068
min          0.000000
25%          0.014286
50%          0.771429
75%          6.785714
max       6087.428571
Name: mean, dtype: float64

All statistics confirm what seen before in the histogram: almost all elements are concentrated around 0.

¶

2.1.2.4) Details of distribution of std

In [ ]:
df_MCF7_unf_transposed_summary.iloc[2].describe()
Out[ ]:
count    22934.000000
mean        50.400187
std        365.511266
min          0.072168
25%          0.985722
50%          9.557268
75%         40.101361
max      27101.548020
Name: std, dtype: float64
In [ ]:
mcf_filtered_transposed_summary.iloc[2].describe()
Out[ ]:
count    18945.000000
mean        59.061355
std        387.511937
min          0.137339
25%          3.331064
50%         17.175465
75%         49.605087
max      27449.497138
Name: std, dtype: float64

From unfiltered to filtered datasets mean of STD increases, implying that datas are more spread.

In [ ]:
mcf_removed_elements_transposed_summary.iloc[2].describe()
Out[ ]:
count    22934.000000
mean        35.803314
std        304.662540
min          0.000000
25%          0.119523
50%          3.421767
75%         24.840822
max      19524.700983
Name: std, dtype: float64

¶

2.1.3) TRIAL AND ERROR OF PSEUDO FILTERING ON CELLS

Now, to understand how filtering proces happen we try to put upper/lower bounds to mean and STD.

To check the similarity between pseudo filtered dataset and filtered one we used the ratio between number of elements in intersection between pseudo filtered and filtered and number of elements in filtered.

Watch also number of removed element at each iteration, elements wrongly filtered (not in filtered but selected in pseudo filtered) and elements that have not been filtered (in filtered but not in pseudo filtered).

In [ ]:
# similarity between filtered and unfiltred data

filtered_set = set(mcf_filtered.columns)
unfiltered_set = set(df_MCF7_unf.columns)

intersection = filtered_set.intersection(unfiltered_set)
union = filtered_set.union(unfiltered_set)

similarity = len(intersection) / len(union)

removed = unfiltered_set - filtered_set

print(f"Similarity Percentage: {similarity}%", "\nnumber of removed:", len(removed))

Similarity Percentage: 0.8172323759791122% 
number of removed: 70
In [ ]:
# trial 1: filter removing cells with mean below 11 and std below 60

# Filter columns based on mean and standard deviation
filtered_columns = df_MCF7_unf.columns[(df_MCF7_unf.mean() >= 11) & (df_MCF7_unf.std() >= 60)]

# Create pseudo_filtered dataset
pseudo_filtered = df_MCF7_unf[filtered_columns]
In [ ]:
pseudo_filtered_summary=pseudo_filtered.describe()
pseudo_filtered.describe()
Out[ ]:
"output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" "output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam" "output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam" "output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam" "output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam" "output.STAR.1_B10_Hypo_S76_Aligned.sortedByCoord.out.bam" "output.STAR.1_B11_Hypo_S77_Aligned.sortedByCoord.out.bam" "output.STAR.1_B12_Hypo_S78_Aligned.sortedByCoord.out.bam" ... "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" "output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam" "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam"
count 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 ... 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000
mean 40.817651 86.442400 14.531351 56.213613 75.397183 62.767725 67.396747 99.728133 70.469957 41.026206 ... 17.362562 42.080230 34.692422 32.735284 21.992718 17.439391 49.242784 61.545609 68.289352 62.851400
std 465.709940 1036.572689 123.800530 503.599145 430.471519 520.167576 459.689019 1035.207158 828.757699 369.992969 ... 193.153757 256.775704 679.960908 300.291051 153.441647 198.179666 359.337479 540.847355 636.892085 785.670341
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
75% 17.000000 5.000000 7.000000 23.000000 39.000000 35.000000 38.000000 43.000000 32.000000 23.000000 ... 9.000000 30.000000 0.000000 17.000000 12.000000 9.000000 27.000000 30.000000 38.000000 33.000000
max 46744.000000 82047.000000 10582.000000 46856.000000 29534.000000 50972.000000 36236.000000 60475.000000 56068.000000 24994.000000 ... 17800.000000 23355.000000 81952.000000 29540.000000 12149.000000 19285.000000 28021.000000 40708.000000 46261.000000 68790.000000

8 rows × 324 columns

In [ ]:
pseudo_filtered_mean_max_value = pseudo_filtered_summary.iloc[1].max()
print("max mean value in pseudo filtered:", pseudo_filtered_mean_max_value)
mcf_filtered_mean_min_value = pseudo_filtered_summary.iloc[1].min()
print("min mean value in pseudo filtered:", mcf_filtered_mean_min_value)

max mean value in pseudo filtered: 100.639094793756
min mean value in pseudo filtered: 11.483779541292405
In [ ]:
sns.displot(pseudo_filtered_summary.iloc[1], bins=100)
plt.xlim(0, 105) 
plt.ylim(0, 20)
plt.title("Distribution of means in pseudo filtered datased based on cells")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
pseudo_filtered_std_max_value = pseudo_filtered_summary.iloc[2].max()
print("max STD value in pseudo filtered:", pseudo_filtered_std_max_value)
mcf_filtered_std_min_value = pseudo_filtered_summary.iloc[2].min()
print("min STD value in pseudo filtered:", mcf_filtered_std_min_value)

max STD value in pseudo filtered: 1668.9422310403038
min STD value in pseudo filtered: 66.39211740865552
In [ ]:
sns.displot(pseudo_filtered_summary.iloc[2], bins=100)
plt.xlim(0, 1700) 
plt.ylim(0, 20)
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
# similarity between filtered and pseudo_filtered data

# Convert the dataframes to sets
filtered_set = set(mcf_filtered.columns)
pseudo_set = set(pseudo_filtered.columns)
unfiltered_set = set(df_MCF7_unf.columns)

# Calculate the intersection and union of the sets
intersection = filtered_set.intersection(pseudo_set)
union = filtered_set.union(pseudo_set)

# Calculate the Jaccard similarity coefficient
similarity = len(intersection) / len(union)

# Convert the similarity to percentage
similarity_percentage = similarity * 100

rem=unfiltered_set - pseudo_set
diff = pseudo_set - filtered_set #elements in pseudo_filtered that are not in filtered
diff1 = filtered_set - pseudo_set #elements in filtered that are not in pseudo_filtered

# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage}%", "\nnumber of removed", len(rem), 
      "\nnumber of elements in pseudo_filtered that are not in filtered:", len(diff), 
      "\nnumber of elemtns in filtered that are not in pseudo_filtered:", len(diff1))

Similarity Percentage: 96.60493827160494% 
number of removed 59 
number of elements in pseudo_filtered that are not in filtered: 11 
number of elemtns in filtered that are not in pseudo_filtered: 0

Given these lower bounds check if there are betters (check if similarity percentage increases or decreases)

In [ ]:
#check 1: reduce mean to 9

# Filter columns based on mean and standard deviation
filtered_columns_check = df_MCF7_unf.columns[(df_MCF7_unf.mean() >= 9)]

# Create pseudo_filtered dataset
pseudo_filtered_check = df_MCF7_unf[filtered_columns_check]

pseudo_filtered_check_summary=pseudo_filtered_check.describe()
pseudo_filtered_check.describe()

# similarity between filtered and pseudo_filtred data

# Convert the dataframes to sets
filtered_set = set(mcf_filtered.columns)
pseudo_set_check = set(pseudo_filtered_check.columns)
unfiltered_set = set(df_MCF7_unf.columns)

# Calculate the intersection and union of the sets
intersection_check = filtered_set.intersection(pseudo_set_check)
union_check = filtered_set.union(pseudo_set_check)

# Calculate the Jaccard similarity coefficient
similarity_check = len(intersection_check) / len(union_check)

# Convert the similarity to percentage
similarity_percentage_check = similarity_check * 100

rem_check=unfiltered_set - pseudo_set_check
diff__check = pseudo_set - filtered_set #elements in pseudo_filtred that are not in filtered
diff1__check = filtered_set - pseudo_set_check #elements in filtered that are not in pseudo_filetred

# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage_check}%", 
        "\nnumber of removed", len(rem_check), 
        "\nnumber of elements in pseudo_filtred that are not in filtered (elements wrongly filtered):", len(diff__check), 
        "\nnumber of elemetns in filtered that are not in pseudo_filtered (elements that still need to filter):", len(diff1__check))

Similarity Percentage: 96.0122699386503% 
number of removed 57 
number of elements in _check that are not in filtered: 11 
number of elemtns in filtered that are not in _check: 0

Similarity Percentage: 96.0122699386503% 
number of removed 57 
number of elements in _check that are not in filtered: 11 
number of elemtns in filtered that are not in pseudo_filtered: 0
In [ ]:
#check 2: reduce std to 40

# Filter columns based on mean and standard deviation
filtered_columns_check2 = df_MCF7_unf.columns[(df_MCF7_unf.std() >= 50)]

# Create pseudo_filtered dataset
pseudo_filtered_check2 = df_MCF7_unf[filtered_columns_check2]

pseudo_filtered_check2_summary=pseudo_filtered_check2.describe()

# similarity between filtered and _check data

# Convert the dataframes to sets
filtered_set = set(mcf_filtered.columns)
pseudo_set_check2 = set(pseudo_filtered_check2.columns)
unfiltered_set = set(df_MCF7_unf.columns)

# Calculate the intersection and union of the sets
intersection_check2 = filtered_set.intersection(pseudo_set_check2)
union_check2 = filtered_set.union(pseudo_set_check2)

similarity_check2 = len(intersection_check2) / len(union_check2)

# Convert the similarity to percentage
similarity_percentage_check2 = similarity_check2 * 100

rem_check2=unfiltered_set - pseudo_set_check2
diff__check2 = pseudo_set - filtered_set #elements in _check that are not in filtered
diff1__check2 = filtered_set - pseudo_set_check2 #elements in filtered that are not in _check

# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage_check2}%", "\nnumber of removed", len(rem_check2), 
      "\nnumber of elements in _check that are not in filtered:", len(diff__check2), 
      "\nnumber of elemtns in filtered that are not in _check:", len(diff1__check2))

Similarity Percentage: 93.7125748502994% 
number of removed 49 
number of elements in _check that are not in filtered: 11 
number of elemtns in filtered that are not in _check: 0
In [ ]:
#check 3: increase mean to 12

# Filter columns based on mean and standard deviation
filtered_columns_check3 = df_MCF7_unf.columns[(df_MCF7_unf.mean() >= 12)]

# Create pseudo_filtered dataset
pseudo_filtered_check3 = df_MCF7_unf[filtered_columns_check3]

pseudo_filtered_check3_summary=pseudo_filtered_check3.describe()

# similarity between filtered and _check data

# Convert the dataframes to sets
filtered_set = set(mcf_filtered.columns)
pseudo_set_check3 = set(pseudo_filtered_check3.columns)
unfiltered_set = set(df_MCF7_unf.columns)

# Calculate the intersection and union of the sets
intersection_check3 = filtered_set.intersection(pseudo_set_check3)
union_check3 = filtered_set.union(pseudo_set_check3)

# Calculate the Jaccard similarity coefficient
similarity_check3 = len(intersection_check3) / len(union_check3)

# Convert the similarity to percentage
similarity_percentage_check3 = similarity_check3 * 100

rem_check3=unfiltered_set - pseudo_set_check3
diff__check3 = pseudo_set - filtered_set #elements in _check that are not in filtered
diff1__check3 = filtered_set - pseudo_set_check3 #elements in filtered that are not in _check

# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage_check3}%", "\nnumber of removed", len(rem_check3), 
      "\nnumber of elements in _check that are not in filtered:", len(diff__check3), 
      "\nnumber of elemtns in filtered that are not in _check:", len(diff1__check3))

# I removed 66 elements. 7 of these are mistakes. still need to remove 11 elements

Similarity Percentage: 95.67901234567901% 
number of removed 62 
number of elements in _check that are not in filtered: 11 
number of elemtns in filtered that are not in _check: 3
In [ ]:
#check 4: increase std to 70

# Filter columns based on mean and standard deviation
filtered_columns_check3 = df_MCF7_unf.columns[(df_MCF7_unf.std() >= 70)]

# Create pseudo_filtered dataset
pseudo_filtered_check3 = df_MCF7_unf[filtered_columns_check3]

pseudo_filtered_check3_summary=pseudo_filtered_check3.describe()

# similarity between filtered and _check data

# Convert the dataframes to sets
filtered_set = set(mcf_filtered.columns)
pseudo_set_check3 = set(pseudo_filtered_check3.columns)
unfiltered_set = set(df_MCF7_unf.columns)

# Calculate the intersection and union of the sets
intersection_check3 = filtered_set.intersection(pseudo_set_check3)
union_check3 = filtered_set.union(pseudo_set_check3)

# Calculate the Jaccard similarity coefficient
similarity_check3 = len(intersection_check3) / len(union_check3)

# Convert the similarity to percentage
similarity_percentage_check3 = similarity_check3 * 100

rem_check3=unfiltered_set - pseudo_set_check3
diff__check3 = pseudo_set - filtered_set #elements in _check that are not in filtered
diff1__check3 = filtered_set - pseudo_set_check3 #elements in filtered that are not in _check

# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage_check3}%", "\nnumber of removed", len(rem_check3), 
      "\nnumber of elements in _check that are not in filtered:", len(diff__check3), 
      "\nnumber of elemtns in filtered that are not in _check:", len(diff1__check3))

Similarity Percentage: 95.41284403669725% 
number of removed 57 
number of elements in _check that are not in filtered: 11 
number of elemtns in filtered that are not in _check: 1

THUS MEAN=11 annd STD=60 are best LOWER BOUNDS

In [ ]:
# trial 2: remove elements with mean over 88

# since element with max std in unfiltered is also in filtered, I will not put an upper bound on std

# Filter columns based on mean and standard deviation
filtered_columns2 = pseudo_filtered.columns[(pseudo_filtered.mean() < 88)]

# Create pseudo_filtered dataset
pseudo_filtered2= df_MCF7_unf[filtered_columns2]
In [ ]:
pseudo_filtered2_summary=pseudo_filtered2.describe()
pseudo_filtered2.describe()
Out[ ]:
"output.STAR.1_A10_Hypo_S28_Aligned.sortedByCoord.out.bam" "output.STAR.1_A12_Hypo_S30_Aligned.sortedByCoord.out.bam" "output.STAR.1_A2_Norm_S2_Aligned.sortedByCoord.out.bam" "output.STAR.1_A3_Norm_S3_Aligned.sortedByCoord.out.bam" "output.STAR.1_A4_Norm_S4_Aligned.sortedByCoord.out.bam" "output.STAR.1_A5_Norm_S5_Aligned.sortedByCoord.out.bam" "output.STAR.1_A6_Norm_S6_Aligned.sortedByCoord.out.bam" "output.STAR.1_B11_Hypo_S77_Aligned.sortedByCoord.out.bam" "output.STAR.1_B12_Hypo_S78_Aligned.sortedByCoord.out.bam" "output.STAR.1_B4_Norm_S52_Aligned.sortedByCoord.out.bam" ... "output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" "output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" "output.STAR.4_H2_Norm_S356_Aligned.sortedByCoord.out.bam" "output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" "output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" "output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" "output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" "output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" "output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" "output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam"
count 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 ... 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000 22934.000000
mean 40.817651 86.442400 14.531351 56.213613 75.397183 62.767725 67.396747 70.469957 41.026206 13.533793 ... 17.362562 42.080230 34.692422 32.735284 21.992718 17.439391 49.242784 61.545609 68.289352 62.851400
std 465.709940 1036.572689 123.800530 503.599145 430.471519 520.167576 459.689019 828.757699 369.992969 146.444225 ... 193.153757 256.775704 679.960908 300.291051 153.441647 198.179666 359.337479 540.847355 636.892085 785.670341
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
75% 17.000000 5.000000 7.000000 23.000000 39.000000 35.000000 38.000000 32.000000 23.000000 5.000000 ... 9.000000 30.000000 0.000000 17.000000 12.000000 9.000000 27.000000 30.000000 38.000000 33.000000
max 46744.000000 82047.000000 10582.000000 46856.000000 29534.000000 50972.000000 36236.000000 56068.000000 24994.000000 13587.000000 ... 17800.000000 23355.000000 81952.000000 29540.000000 12149.000000 19285.000000 28021.000000 40708.000000 46261.000000 68790.000000

8 rows × 320 columns

In [ ]:
pseudo_filtered2_mean_max_value = pseudo_filtered2_summary.iloc[1].max()
print("max mean value in pseudo filtered2:", pseudo_filtered2_mean_max_value)
mcf_filtered2_mean_min_value = pseudo_filtered2_summary.iloc[1].min()
print("min mean value in pseudo filtered2:", mcf_filtered2_mean_min_value)

max mean value in pseudo filtered2: 86.44239993023459
min mean value in pseudo filtered2: 11.483779541292405
In [ ]:
sns.displot(pseudo_filtered2_summary.iloc[1], bins=100)
plt.xlim(0, 90) 
plt.ylim(0, 30)
plt.title("Distribution of means in pseudo_filtered2")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
pseudo_filtered2_std_max_value = pseudo_filtered2_summary.iloc[2].max()
print("max std value in pseudo filtered2:", pseudo_filtered2_std_max_value)
mcf_filtered2_std_min_value = pseudo_filtered2_summary.iloc[2].min()
print("min std value in pseudo filtered2:", mcf_filtered2_std_min_value)

max std value in pseudo filtered2: 1668.9422310403038
min std value in pseudo filtered2: 66.39211740865552
In [ ]:
sns.displot(pseudo_filtered2_summary.iloc[2], bins=100)
plt.xlim(0, 1700) 
plt.ylim(0, 20)
plt.title("Distribution of STD on pseudo_filtered2")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
# similarity between filtered and pseudo_filtered2 data

# Convert the dataframes to sets
filtered_set = set(mcf_filtered.columns)
pseudo_set2 = set(pseudo_filtered2.columns)
unfiltered_set = set(df_MCF7_unf.columns)

# Calculate the intersection and union of the sets
intersection2 = filtered_set.intersection(pseudo_set2)
union2 = filtered_set.union(pseudo_set2)

# Calculate the Jaccard similarity coefficient
similarity2 = len(intersection2) / len(union2)

# Convert the similarity to percentage
similarity_percentage2 = similarity2 * 100

rem2=unfiltered_set - pseudo_set2
diff_2 = pseudo_set2 - filtered_set #elements in pseudo_filtered3 that are not in filtered
diff1_2 = filtered_set - pseudo_set2 # in filtered that are not in pseudo_filtered3

# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage2}%", "\nnumber of removed", len(rem2),
      "\nnumber of elements in pseudo_filtered2 that are not in filtered:", len(diff_2), 
      "\nnumber of elemtns in filtered that are not in pseudo_filtered2:", len(diff1_2))

# I removed 66 elements. 7 of these are mistakes. still need to remove 11 elements

Similarity Percentage: 97.8125% 
number of removed 63 
number of elements in pseudo_filtered2 that are not in filtered: 7 
number of elemtns in filtered that are not in pseudo_filtered2: 0

Check wheter mean=88 is bood upper bound

In [ ]:
# check 1: reduce mean to 85
# any value of mean above 88 gives same result no other check needed

# Filter columns based on mean and standard deviation
filtered_columns4 = pseudo_filtered2.columns[(pseudo_filtered2.mean() < 85)]

# Create pseudo_filtered dataset
pseudo_filtered4 = df_MCF7_unf[filtered_columns4]

pseudo_filtered4_summary=pseudo_filtered4.describe()

# similarity between filtered and pseudo_filtered4 data

# Convert the dataframes to sets
filtered_set = set(mcf_filtered.columns)
pseudo_set4 = set(pseudo_filtered4.columns)
unfiltered_set = set(df_MCF7_unf.columns)

# Calculate the intersection and union of the sets
intersection4 = filtered_set.intersection(pseudo_set4)
union4 = filtered_set.union(pseudo_set4)

# Calculate the Jaccard similarity coefficient
similarity4 = len(intersection4) / len(union4)

# Convert the similarity to percentage
similarity_percentage4 = similarity4 * 100

rem4=unfiltered_set - pseudo_set4
diff_4 = pseudo_set4 - filtered_set #elements in pseudo_filtered3 that are not in filtered
diff1_4 = filtered_set - pseudo_set4 #elements in filtered that are not in pseudo_filtered3

# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage4}%", "\nnumber of removed", len(rem4), 
      "\nnumber of elements in pseudo_filtered2 that are not in filtered:", len(diff_4), 
      "\nnumber of elemtns in filtered that are not in pseudo_filtered2:", len(diff1_4))

Similarity Percentage: 97.1875% 
number of removed 65 
number of elements in pseudo_filtered2 that are not in filtered: 7 
number of elemtns in filtered that are not in pseudo_filtered2: 2
¶

FILTERING MANTAINING CELLS WITH MEAN OVER GENES BETWEEN 11 AND 88 AND STD OVER 60 WE REACH A SIMILIARITY PERCENTAGE WITH FILTERED DATAFRAME OF 97.8125%:

  • 62 REMOVED ELEMENTS
  • 8 ELEMENTS WRONGLY FILTERED
  • 0 MISSING FILTERS

¶

2.1.4) TRIAL AND ERROR OF PSEUDO FILTERING ON GENES

We follow same approach as before applied on genes

In [ ]:
# similarity between filtered_transposed and unfiltred_transposed data

filtered_transposed_set=set(mcf_filtered_transposed.columns)
unfiltered_transposed_set=set(df_MCF7_unf_transposed.columns)

intersection=filtered_transposed_set.intersection(unfiltered_transposed_set)
union=filtered_transposed_set.union(unfiltered_transposed_set)

similarity_transposed=len(intersection)/len(union)
removed_transposed=unfiltered_transposed_set - filtered_transposed_set

print(f"Similarity Percentage: {similarity_transposed}%", "\nnumber of removed:", len(removed_transposed))

Similarity Percentage: 0.8260661027295718% 
number of removed: 3989
In [ ]:
#trial 1: filter genes with mean below 0.1

filtered_transposed_columns = df_MCF7_unf_transposed.columns[(df_MCF7_unf_transposed.mean() >= 0.1)]
pseudo_transposed_filtered = df_MCF7_unf_transposed[filtered_transposed_columns]
pseudo_transposed_filtered_summary=pseudo_transposed_filtered.describe()
pseudo_transposed_filtered.describe()
Out[ ]:
"WASH7P" "WASH9P" "MTND2P28" "MTCO1P12" "MTCO2P12" "MTATP6P1" "MTCO3P12" "LINC01409" "FAM87B" "LINC01128" ... "MT-TH" "MT-TS2" "MT-TL2" "MT-ND5" "MT-ND6" "MT-TE" "MT-CYB" "MT-TT" "MT-TP" "MAFIP"
count 383.000000 383.000000 383.000000 383.000000 383.000000 383.000000 383.000000 383.000000 383.000000 383.000000 ... 383.000000 383.000000 383.000000 383.000000 383.000000 383.000000 383.00000 383.000000 383.000000 383.000000
mean 0.133159 1.344648 6.261097 4.681462 0.524804 222.054830 0.433420 0.174935 0.302872 11.872063 ... 1.060052 0.443864 3.146214 1016.477807 204.600522 5.049608 2374.97389 2.083551 5.626632 1.749347
std 0.618664 2.244543 7.565749 6.232649 0.980857 262.616874 0.970848 0.711314 1.418711 18.673043 ... 1.990566 1.090827 4.265352 1009.444811 220.781927 6.644302 2920.39000 3.372714 7.511180 3.895204
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 23.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 172.000000 30.500000 0.000000 216.50000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 3.000000 2.000000 0.000000 98.000000 0.000000 0.000000 0.000000 3.000000 ... 0.000000 0.000000 1.000000 837.000000 152.000000 3.000000 785.00000 0.000000 3.000000 0.000000
75% 0.000000 2.000000 10.000000 7.000000 1.000000 370.500000 1.000000 0.000000 0.000000 18.000000 ... 1.000000 0.000000 5.000000 1549.000000 294.000000 7.000000 4059.00000 3.000000 8.000000 2.000000
max 9.000000 20.000000 45.000000 36.000000 6.000000 1662.000000 10.000000 6.000000 17.000000 153.000000 ... 15.000000 8.000000 22.000000 8115.000000 2002.000000 46.000000 16026.00000 22.000000 56.000000 32.000000

8 rows × 17764 columns

In [ ]:
pseudo_filtered_transposed_mean_max_value = pseudo_transposed_filtered_summary.iloc[1].max()
print("max mean value in pseudo filtered:", pseudo_filtered_transposed_mean_max_value)
mcf_filtered_transposed_mean_min_value = pseudo_transposed_filtered_summary.iloc[1].min()
print("min mean value in pseudo filtered:", mcf_filtered_transposed_mean_min_value)

max mean value in pseudo filtered: 23052.762402088774
min mean value in pseudo filtered: 0.10182767624020887
In [ ]:
sns.displot(pseudo_transposed_filtered_summary.iloc[1], bins=500)
plt.xlim(0, 25000) 
plt.ylim(0, 1000)
plt.title("Distribution of means over pseudo_transposed_filtered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
pseudo_filtered_transposed_std_max_value = pseudo_transposed_filtered_summary.iloc[2].max()
print("max std value in pseudo filtered:", pseudo_filtered_transposed_std_max_value)
mcf_filtered_transposed_mean_min_value = pseudo_transposed_filtered_summary.iloc[2].min()
print("min std value in pseudo filtered:", mcf_filtered_transposed_std_min_value)

max std value in pseudo filtered: 27101.548019606067
min std value in pseudo filtered: 0.13733946369142574
In [ ]:
sns.displot(pseudo_transposed_filtered_summary.iloc[2], bins=500)
plt.xlim(0, 27200) 
plt.ylim(0, 1000)
plt.title("Distribution of STD over pseudo_transposed_filtered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
# similarity between filtered_transposed and pseudo_transposed_filtered data

filtered_transposed_set=set(mcf_filtered_transposed.columns)
pseudo_transposed_filtered_set=set(pseudo_transposed_filtered.columns)
unfiltered_transposed_set=set(df_MCF7_unf_transposed.columns)

intersection=filtered_transposed_set.intersection(pseudo_transposed_filtered_set)
union=filtered_transposed_set.union(pseudo_transposed_filtered)

similarity_transposed=len(intersection)/len(union)
similarity_percentage_transposed=similarity_transposed*100

removed=unfiltered_transposed_set - pseudo_transposed_filtered_set
diff_transposed=pseudo_transposed_filtered_set-filtered_transposed_set
diff1_transposed=filtered_transposed_set-pseudo_transposed_filtered_set

print(f"Similarity Percentage: {similarity_percentage_transposed}%", "\nnumber of removed:", len(removed), 
      "\nnumber of elements in pseudo_filtered_transposed that are not in filtered_transposed:", len(diff_transposed), 
      "\nnumber of elemtns in filtered_transposed that are not in pseudo_filtered_transposed:", len(diff1_transposed))

Similarity Percentage: 88.657621543838% 
number of removed: 5170 
number of elements in pseudo_filtered_transposed that are not in filtered_transposed: 513 
number of elemtns in filtered_transposed that are not in pseudo_filtered_transposed: 1694

Check whether mean=0.1 is good lowe bound

Note: with a lower threshold we would go back to original dataset with similarity 85%

In [ ]:
# check 2 higher thresholds (mean=0.5. std=1)

filtered_transposed_columns_check = df_MCF7_unf_transposed.columns[(df_MCF7_unf_transposed.mean() >= 0.5) & (df_MCF7_unf_transposed.std() >= 0.1)]
pseudo_transposed_filtered_check = df_MCF7_unf_transposed[filtered_transposed_columns_check]
pseudo_transposed_filtered_check_summary=pseudo_transposed_filtered_check.describe()

# similarity between filtered_transposed and pseudo_transposed_filtered data

filtered_transposed_set=set(mcf_filtered_transposed.columns)
pseudo_transposed_filtered_check_set=set(pseudo_transposed_filtered_check.columns)
unfiltered_transposed_set=set(df_MCF7_unf_transposed.columns)

intersection_check=filtered_transposed_set.intersection(pseudo_transposed_filtered_check_set)
union_check=filtered_transposed_set.union(pseudo_transposed_filtered_check)

similarity_transposed_check=len(intersection_check)/len(union_check)
similarity_percentage_transposed_check=similarity_transposed_check*100

removed_check=unfiltered_transposed_set - pseudo_transposed_filtered_check_set
diff_transposed_check=pseudo_transposed_filtered_check_set-filtered_transposed_set
diff1_transposed_check=filtered_transposed_set-pseudo_transposed_filtered_check_set

print(f"Similarity Percentage: {similarity_percentage_transposed_check}%", "\nnumber of removed:", len(removed_check), 
      "\nnumber of elements in pseudo_filtered_transposed that are not in filtered_transposed:", len(diff_transposed_check), 
      "\nnumber of elemtns in filtered_transposed that are not in pseudo_filtered_transposed:", len(diff1_transposed_check))

Similarity Percentage: 78.36997205967631% 
number of removed: 8044 
number of elements in pseudo_filtered_transposed that are not in filtered_transposed: 24 
number of elemtns in filtered_transposed that are not in pseudo_filtered_transposed: 4079

mean=0.1, std=0 is best lower bound

Furtermre, best option is to put no upper bound on mean and std since smiliarity will decrease. Infact:

In [ ]:
# put upper bound mean=26900, std=27100 (just 1 element is removed)

filtered_transposed_column_check2 = pseudo_transposed_filtered.columns[(pseudo_transposed_filtered.mean() <= 26900) & (pseudo_transposed_filtered.std() <= 27100)]
pseudo_transposed_filtered_check2 = pseudo_transposed_filtered[filtered_transposed_column_check2]
pseudo_transposed_filtered_check2_summary=pseudo_transposed_filtered_check2.describe()


#similarity between filtered_transposed and pseudo_transposed_filtered data

filtered_transposed_set=set(mcf_filtered_transposed.columns)
pseudo_transposed_filtered_set_check2=set(pseudo_transposed_filtered_check2.columns)
unfiltered_transposed_set=set(df_MCF7_unf_transposed.columns)

intersection_check2=filtered_transposed_set.intersection(pseudo_transposed_filtered_set_check2)
union_check2=filtered_transposed_set.union(pseudo_transposed_filtered_check2)

similarity_transposed_check2=len(intersection_check2)/len(union_check2)
similarity_percentage_transposed_check2=similarity_transposed_check2*100

removed_check2=unfiltered_transposed_set - pseudo_transposed_filtered_set_check2
diff_transposed_check2=pseudo_transposed_filtered_set_check2-filtered_transposed_set
diff1_transposed_check2=filtered_transposed_set-pseudo_transposed_filtered_set_check2

print(f"Similarity Percentage: {similarity_percentage_transposed_check2}%", "\nnumber of removed:", len(removed_check2), 
      "\nnumber of elements in pseudo_filtered_transposed that are not in filtered_transposed:", len(diff_transposed_check2), 
      "\nnumber of elemtns in filtered_transposed that are not in pseudo_filtered_transposed:", len(diff1_transposed_check2))

Similarity Percentage: 88.65248226950354% 
number of removed: 5171 
number of elements in pseudo_filtered_transposed that are not in filtered_transposed: 513 
number of elemtns in filtered_transposed that are not in pseudo_filtered_transposed: 1695

On genes the only filter is mean grater than 0.1 and we will reach Similarity Percentage: 88.657621543838%

¶

2.1.4bis) Merging the two approaches

Intersection based on cell (pseudo_filtered4 intersects pseudo_transposed_filtered transposed)

In [ ]:
intersection_cell = pseudo_filtered.columns.intersection(pseudo_transposed_filtered.T.columns)


intersection_cell_set=set(intersection_cell)
filtered_set=set(mcf_filtered.columns)
unfiltered_set=set(df_MCF7_unf.columns)

int=intersection_cell_set.intersection(filtered_set)
un=intersection_cell_set.union(filtered_set)

similarity_cell = len(int)/len(un)
similarity_cell_percentage = similarity_cell*100

removed_cell = unfiltered_set - intersection_cell_set
diff_cell = filtered_set - intersection_cell_set
diff1_cell = intersection_cell_set - filtered_set

print(f"Similarity Percentage: {similarity_cell_percentage}%", "\nnumber of removed", len(removed_cell),  
      "\nnumber of elements in cell intersection that are not in filtered:", len(diff_cell), 
        "\nnumber of elemtns in filtered that are not in cell intersection:", len(diff1_cell))

Similarity Percentage: 96.60493827160494% 
number of removed 59 
number of elements in cell intersection that are not in filtered: 0 
number of elemtns in filtered that are not in cell intersection: 11

intersection based on genes (pseudo_filtered4 transposef intersects pseudo_transposed_filtered)

In [ ]:
genes_pseudo4=pseudo_filtered2.T
intersection_genes= genes_pseudo4.columns.intersection(pseudo_transposed_filtered.columns)


intersection_genes_set=set(intersection_genes)
filtered_set_transposed=set(mcf_filtered_transposed.columns)
unfiltered_set_transposed=set(df_MCF7_unf_transposed.columns)

int_genes=intersection_genes_set.intersection(filtered_set_transposed)
un_genes=intersection_genes_set.union(filtered_set_transposed)

similarity_genes = len(int_genes)/len(un_genes)
similarity_genes_percentage = similarity_genes*100

removed_genes = unfiltered_set_transposed - intersection_genes_set
diff_genes = filtered_set_transposed - intersection_genes_set
diff1_genes = intersection_genes_set - filtered_set_transposed

print(f"Similarity Percentage: {similarity_genes_percentage}%", "\nnumber of removed", len(removed_genes),  
      "\nnumber of elements in genes intersection that are not in filtered:", len(diff_genes), 
        "\nnumber of elemtns in filtered that are not in genes intersection:", len(diff1_genes))

Similarity Percentage: 88.657621543838% 
number of removed 5170 
number of elements in genes intersection that are not in filtered: 1694 
number of elemtns in filtered that are not in genes intersection: 513

RESULT: best result is obtaining by filtering only on cells with mean between 11 and 88 and std over 60

¶

2.1.5) PCA

In [ ]:
df_original = pd.read_csv("SmartSeq/MCF7_SmartS_Unfiltered_Data.txt",delimiter="\ ",engine='python',index_col=0)
df_filtered = pd.read_csv("SmartSeq/MCF7_SmartS_Filtered_Data.txt",delimiter="\ ",engine='python',index_col=0)
In [ ]:
# PCA to reduce dimensionality
pca = PCA(n_components=50)  # Reducing to 50 principal components
data_filtered_reduced = pca.fit_transform(df_filtered.T)
data_original_reduced = pca.fit_transform(df_original.T)
In [ ]:
def extract_condition(name):
    match = re.search(r'(Norm|Hypo)', name)
    if match:
        return match.group(1)
    return 'Unknown'
In [ ]:
conditions = df_filtered.T.index.to_series().apply(extract_condition)
conditions_original = df_original.T.index.to_series().apply(extract_condition)
In [ ]:
plt.figure(figsize=(12, 6))

# Assign colors to conditions manually
color_map = {'Hypo': 'red', 'Norm': 'blue', 'Unknown': 'green'}
colors = conditions.map(color_map)

plt.scatter(data_filtered_reduced[:, 0], data_filtered_reduced[:, 1], c=colors)
plt.title('PCA of Cells Colored by Condition')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')

# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i], 
            label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')

plt.show()
No description has been provided for this image

From this PCA it is possible to obvserve that the data has been filtered in such a way that it is more easily separable. We can suppose the least expressive genes have been cut out, and looking at the PCA we can see how the two conditions appear to be almost linearly separable already in twpo dimension.

¶

2.1.6) Clustering

¶

2.1.7.1) Single linkage

In [ ]:
# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_filtered_reduced, 'single')

# Increase the figure size
plt.figure(figsize=(25, 10))  # You can adjust the size as necessary.

# Plotting a truncated dendrogram
dendrogram(
    Z,
    truncate_mode='lastp',  # show only the last p merged clusters
    p=50,  # show only the last 50 merged clusters
    leaf_rotation=90.,
    leaf_font_size=12.,
    show_contracted=True,  # to get a sense of the dataset size if truncated
)

plt.title('Hierarchical Clustering Dendrogram (Single-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
No description has been provided for this image
In [ ]:
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage

# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_original_reduced, 'single')

# Increase the figure size
plt.figure(figsize=(25, 10))  # You can adjust the size as necessary.

# Plotting a truncated dendrogram
dendrogram(
    Z,
    truncate_mode='lastp',  # show only the last p merged clusters
    p=50,  # show only the last 50 merged clusters
    leaf_rotation=90.,
    leaf_font_size=12.,
    show_contracted=True,  # to get a sense of the dataset size if truncated
)

plt.title('Hierarchical Clustering Dendrogram (Single-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
No description has been provided for this image

¶

2.1.7.2) Average Linkage

In [ ]:
# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_filtered_reduced, 'average')

# Increase the figure size
plt.figure(figsize=(25, 10))  # You can adjust the size as necessary.

# Plotting a truncated dendrogram
dendrogram(
    Z,
    truncate_mode='lastp',  # show only the last p merged clusters
    p=50,  # show only the last 50 merged clusters
    leaf_rotation=90.,
    leaf_font_size=12.,
    show_contracted=True,  # to get a sense of the dataset size if truncated
)

plt.title('Hierarchical Clustering Dendrogram (Average-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
No description has been provided for this image
In [ ]:
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage

# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_original_reduced, 'average')

# Increase the figure size
plt.figure(figsize=(25, 10))  # You can adjust the size as necessary.

# Plotting a truncated dendrogram
dendrogram(
    Z,
    truncate_mode='lastp',  # show only the last p merged clusters
    p=50,  # show only the last 50 merged clusters
    leaf_rotation=90.,
    leaf_font_size=12.,
    show_contracted=True,  # to get a sense of the dataset size if truncated
)

plt.title('Hierarchical Clustering Dendrogram (-Average-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
No description has been provided for this image

¶

2.1.7.3) Complete Linkage

In [ ]:
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage

# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_filtered_reduced, 'complete')

# Increase the figure size
plt.figure(figsize=(25, 10))  # You can adjust the size as necessary.

# Plotting a truncated dendrogram
dendrogram(
    Z,
    truncate_mode='lastp',  # show only the last p merged clusters
    p=50,  # show only the last 50 merged clusters
    leaf_rotation=90.,
    leaf_font_size=12.,
    show_contracted=True,  # to get a sense of the dataset size if truncated
)

plt.title('Hierarchical Clustering Dendrogram (Complete-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
No description has been provided for this image
In [ ]:
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage

# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_original_reduced, 'complete')

# Increase the figure size
plt.figure(figsize=(25, 10))  # You can adjust the size as necessary.

# Plotting a truncated dendrogram
dendrogram(
    Z,
    truncate_mode='lastp',  # show only the last p merged clusters
    p=50,  # show only the last 50 merged clusters
    leaf_rotation=90.,
    leaf_font_size=12.,
    show_contracted=True,  # to get a sense of the dataset size if truncated
)

plt.title('Hierarchical Clustering Dendrogram (Complete-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
No description has been provided for this image

Observing the hierarchical custering dendrograms of both the filtered and the unfiltered data we can make the following observations:

  • the distance between clusters is generally lower in the filtered data
  • in particular in the average-linkage and complete-linkage cases, the number of clusters is reduced This again suggests that the least expressive genes have been cut out during the filtering process, in order to make the remaining data more relevant with respect to the goal of the study.

¶

2.1.7) Unsupervised learning applied to MCF7

In [ ]:
# Function to extract 'Normoxia' or 'Hypoxia' from a cell name
def extract_condition(name):
    match = re.search(r'(Norm|Hypo)', name)
    if match:
        return match.group(1)
    return 'Unknown'
In [ ]:
df_MCF7_norm_filt = pd.read_csv("SmartSeq/MCF7_SmartS_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ",engine='python',index_col=0)
df_MCF7_norm_filt_transpose = df_MCF7_norm_filt.T
# We transpose the original dataset so we make the cells to be the rows since they represents the observations
# and the genes to be columns since they are the features
In [ ]:
# Apply the extract_condition function to each cell name in the DataFrame's index
conditions = df_MCF7_norm_filt_transpose.index.to_series().apply(extract_condition)
In [ ]:
# We apply the pca to our dataset
# We basically reduce the dimensionality of our dataset to 2 dimensions
# because we want to see the data in a 2D space

pca = PCA(n_components=2)
principal_components = pca.fit_transform(df_MCF7_norm_filt_transpose)
In [ ]:
# now based on what we have reduced we apply the k-means to try to clusters cells in two groups
# The groups should be based on the cell with Normoxia and Hypoxia
# Please notice that this k-means should use the so called k++ to initialize the centroids
# If you don't know what k++ is, please add init keywords and set it to something different
k = 2
kmeans = KMeans(n_clusters=k, init='k-means++', random_state=42)
clusters = kmeans.fit_predict(principal_components)

c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
In [ ]:
# Assign numeric labels based on conditions
condition_numeric = conditions.map({'Norm': 0, 'Hypo': 1})
In [ ]:
plt.figure(figsize=(12, 6))

# Assign colors to conditions manually
color_map = {'Hypo': 'red', 'Norm': 'blue', 'Unknown': 'green'}
colors = conditions.map(color_map)

plt.scatter(principal_components[:, 0], principal_components[:, 1], c=colors)
plt.title('PCA of Cells Colored by Condition')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')

# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i], 
            label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')

plt.show()
No description has been provided for this image
In [ ]:
# Visualization of PCA with K-means clusters, with a proper legend
plt.figure(figsize=(12, 6))

# Assign colors based on the K-means cluster result
# Here we assign 'red' to cluster 1 and 'blue' to cluster 0
cluster_colors = ['green' if label == 0 else 'yellow' for label in clusters]

# Scatter plot with assigned colors
scatter = plt.scatter(principal_components[:, 0], principal_components[:, 1], c=cluster_colors)

# Create a legend for the colors
plt.title('PCA of Cells Colored by K-means Clusters')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')

# Assuming that after any potential inversion, cluster 0 corresponds to Normoxia and cluster 1 to Hypoxia
legend_labels = {0: 'Cluster1', 1: 'Cluster2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
                      markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='Condition')

plt.show()
No description has been provided for this image

Here the clusters given by k-means are remarkably similar to the actual suddivision of cells in hypoxic and normoxic: this shows that, along the two principal component given by the PCA, the cells are extremely separated in the two groups.

¶

2.1.8) Looking for most useful and expressed genes across cells

In [ ]:
#I want to split the dataset in 2 parts, one with RNA with hypoxia in the name and the other with RNA with normoxia in the name
hypoxia_samples = df_MCF7_norm_filt.T[df_MCF7_norm_filt.T.index.str.contains('Hypo')]
normoxia_samples = df_MCF7_norm_filt.T[df_MCF7_norm_filt.T.index.str.contains('Norm')]
In [ ]:
hypoxia_samples
Out[ ]:
"CYP1B1" "CYP1B1-AS1" "CYP1A1" "NDRG1" "DDIT4" "PFKFB3" "HK2" "AREG" "MYBL2" "ADM" ... "CD27-AS1" "DNAI7" "MAFG" "LZTR1" "BCO2" "GRIK5" "SLC25A27" "DENND5A" "CDK5R1" "FAM13A-AS1"
"output.STAR.2_B7_Hypo_S79_Aligned.sortedByCoord.out.bam" 5817 2669 0 654 2484 1603 748 436 0 0 ... 0 0 63 0 0 0 0 60 0 0
"output.STAR.2_B9_Hypo_S81_Aligned.sortedByCoord.out.bam" 3841 1565 79 1263 2596 2795 3970 373 554 31 ... 0 0 102 42 0 0 0 107 0 0
"output.STAR.2_C10_Hypo_S130_Aligned.sortedByCoord.out.bam" 9263 3866 238 2634 1323 2624 1265 646 0 0 ... 0 0 92 0 0 0 0 74 0 0
"output.STAR.2_C11_Hypo_S131_Aligned.sortedByCoord.out.bam" 21543 9113 443 540 2044 3449 2034 0 0 2170 ... 4 0 120 56 0 0 0 127 0 13
"output.STAR.2_C8_Hypo_S128_Aligned.sortedByCoord.out.bam" 1338 554 14 634 3513 1360 303 558 178 994 ... 0 0 46 5 0 0 0 14 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
"output.STAR.4_H10_Hypo_S382_Aligned.sortedByCoord.out.bam" 7890 3647 86 481 3692 4702 1011 2707 144 385 ... 0 0 0 20 0 0 0 179 0 0
"output.STAR.4_H14_Hypo_S383_Aligned.sortedByCoord.out.bam" 4512 2035 1654 1052 2410 2561 999 717 175 612 ... 0 0 62 22 0 0 0 97 0 0
"output.STAR.4_H7_Hypo_S379_Aligned.sortedByCoord.out.bam" 34565 13717 11274 1263 4256 5663 701 0 0 0 ... 0 0 45 0 0 0 0 140 0 32
"output.STAR.4_H8_Hypo_S380_Aligned.sortedByCoord.out.bam" 20024 7835 563 925 12733 2187 1141 0 20 81 ... 0 0 57 8 0 0 0 7 0 11
"output.STAR.4_H9_Hypo_S381_Aligned.sortedByCoord.out.bam" 5953 2367 522 1572 2275 3269 2247 169 2 0 ... 0 0 165 0 0 0 0 37 0 12

124 rows × 3000 columns

In [ ]:
hypoxia_summary = hypoxia_samples.describe()
normoxia_summary = normoxia_samples.describe()

normoxia_summary
Out[ ]:
"CYP1B1" "CYP1B1-AS1" "CYP1A1" "NDRG1" "DDIT4" "PFKFB3" "HK2" "AREG" "MYBL2" "ADM" ... "CD27-AS1" "DNAI7" "MAFG" "LZTR1" "BCO2" "GRIK5" "SLC25A27" "DENND5A" "CDK5R1" "FAM13A-AS1"
count 126.000000 126.000000 126.000000 126.000000 126.000000 126.000000 126.000000 126.000000 126.000000 126.000000 ... 126.000000 126.000000 126.000000 126.000000 126.000000 126.000000 126.000000 126.000000 126.000000 126.000000
mean 296.079365 121.126984 1.111111 18.603175 344.960317 157.769841 28.817460 112.238095 648.317460 1.746032 ... 26.507937 0.111111 27.436508 28.849206 0.079365 0.031746 0.071429 30.119048 4.206349 0.388889
std 301.404382 124.625325 7.706851 44.026734 620.660453 289.259279 53.607671 246.050968 582.416478 6.318147 ... 29.943613 1.247219 25.844921 37.043772 0.601374 0.356348 0.801784 29.118958 9.571054 2.342553
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 36.500000 14.000000 0.000000 0.000000 22.000000 24.250000 0.000000 0.000000 144.000000 0.000000 ... 1.000000 0.000000 4.250000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000
50% 228.000000 99.000000 0.000000 1.000000 97.000000 71.000000 8.500000 2.000000 555.000000 0.000000 ... 18.500000 0.000000 22.500000 15.000000 0.000000 0.000000 0.000000 23.500000 0.000000 0.000000
75% 422.250000 175.000000 0.000000 16.250000 317.250000 156.500000 34.500000 73.750000 966.750000 0.000000 ... 39.000000 0.000000 41.750000 45.250000 0.000000 0.000000 0.000000 51.000000 0.000000 0.000000
max 1522.000000 596.000000 78.000000 296.000000 3506.000000 2111.000000 284.000000 1673.000000 3108.000000 41.000000 ... 165.000000 14.000000 125.000000 196.000000 6.000000 4.000000 9.000000 115.000000 55.000000 18.000000

8 rows × 3000 columns

In [ ]:
# I want to do the mean of the mean in the hypoxia samples and return the value
hypoxia_mean = hypoxia_samples.describe().loc['mean'].mean()
hypoxia_mean
Out[ ]:
139.35825268817206
In [ ]:
# Now let's dothe same with the standard deviation
hypoxia_std = hypoxia_samples.describe().loc['std'].mean()
hypoxia_std
Out[ ]:
135.42901730349217
In [ ]:
normoxia_samples
Out[ ]:
"CYP1B1" "CYP1B1-AS1" "CYP1A1" "NDRG1" "DDIT4" "PFKFB3" "HK2" "AREG" "MYBL2" "ADM" ... "CD27-AS1" "DNAI7" "MAFG" "LZTR1" "BCO2" "GRIK5" "SLC25A27" "DENND5A" "CDK5R1" "FAM13A-AS1"
"output.STAR.2_B3_Norm_S57_Aligned.sortedByCoord.out.bam" 343 140 0 0 386 75 0 0 476 0 ... 63 0 17 59 0 0 0 51 0 0
"output.STAR.2_B4_Norm_S58_Aligned.sortedByCoord.out.bam" 131 59 0 1 289 42 0 856 1586 0 ... 0 0 1 25 0 0 0 34 0 0
"output.STAR.2_B5_Norm_S59_Aligned.sortedByCoord.out.bam" 452 203 0 0 0 0 0 0 775 0 ... 15 0 23 0 0 0 0 0 0 0
"output.STAR.2_B6_Norm_S60_Aligned.sortedByCoord.out.bam" 27 7 0 0 288 214 15 242 1191 0 ... 1 0 3 10 0 0 0 4 0 0
"output.STAR.2_C1_Norm_S103_Aligned.sortedByCoord.out.bam" 1013 459 0 0 36 75 15 6 1120 0 ... 0 0 80 0 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
"output.STAR.4_H1_Norm_S355_Aligned.sortedByCoord.out.bam" 160 75 0 0 800 1041 39 0 0 0 ... 35 0 0 35 0 0 0 0 26 0
"output.STAR.4_H3_Norm_S357_Aligned.sortedByCoord.out.bam" 351 138 0 0 1 57 26 0 331 0 ... 10 0 12 2 0 0 0 35 0 0
"output.STAR.4_H4_Norm_S358_Aligned.sortedByCoord.out.bam" 327 130 0 54 189 235 232 1 1 0 ... 13 0 26 0 0 0 0 56 0 0
"output.STAR.4_H5_Norm_S359_Aligned.sortedByCoord.out.bam" 196 102 1 243 266 278 78 1 199 0 ... 79 0 1 0 0 0 0 45 19 0
"output.STAR.4_H6_Norm_S360_Aligned.sortedByCoord.out.bam" 504 238 0 62 417 932 275 0 33 0 ... 86 0 0 0 0 0 0 0 0 0

126 rows × 3000 columns

In [ ]:
normoxia_mean = normoxia_samples.describe().loc['mean'].mean()
normoxia_mean
Out[ ]:
92.81419841269842
In [ ]:
normoxia_std = normoxia_samples.describe().loc['std'].mean()
normoxia_std
Out[ ]:
75.52590828017712
In [ ]:
#we look at how many genes have a mean of expression greater than 1 and we print their names
#for hypoxia samples    
hypoxia_samples_summary = hypoxia_samples.describe()

hypo_genes = hypoxia_samples_summary.loc['mean'][hypoxia_samples_summary.loc['mean'] > 139.358]
hypo_genes_sorted = sorted(hypo_genes.items(), key=lambda x: x[1], reverse=True)
print(hypo_genes_sorted)
print(len(hypo_genes_sorted)) # 384 genes overcome the mean threshold

## Now we apply the sqame with the standard deviation
hypo_genes_std = hypoxia_samples_summary.loc['std'][hypoxia_samples_summary.loc['std'] > 135.429]
hypo_genes_std_sorted = sorted(hypo_genes_std.items(), key=lambda x: x[1], reverse=True)
print(hypo_genes_std_sorted)
print(len(hypo_genes_std_sorted)) # 520 genes overcome the mean std threshold

[('"GAPDH"', 46292.032258064515), ('"ACTG1"', 26915.354838709678), ('"ALDOA"', 26235.370967741936), ('"FTH1"', 10966.370967741936), ('"CYP1B1"', 10696.193548387097), ('"BEST1"', 9709.016129032258), ('"PGK1"', 8696.25), ('"GPI"', 7138.314516129032), ('"UBC"', 6946.177419354839), ('"LDHA"', 5867.25), ('"FTL"', 4929.6612903225805), ('"DDIT4"', 4663.5887096774195), ('"CYP1B1-AS1"', 4430.491935483871), ('"MT-ND4"', 3267.1774193548385), ('"CYP1A1"', 3233.9112903225805), ('"TMSB10"', 2976.8709677419356), ('"PFKFB3"', 2855.6532258064517), ('"SLC3A2"', 2535.032258064516), ('"SLC2A1"', 2530.8709677419356), ('"MT-RNR2"', 2515.3951612903224), ('"MT-CO1"', 2497.6612903225805), ('"DSP"', 2441.1935483870966), ('"HSPB1"', 2355.532258064516), ('"NCOA3"', 2331.0887096774195), ('"KRT80"', 2217.8951612903224), ('"BNIP3"', 2209.5887096774195), ('"CTSD"', 2168.467741935484), ('"FLNA"', 2072.1209677419356), ('"HSPA5"', 2041.0887096774193), ('"SQSTM1"', 1990.008064516129), ('"PSAP"', 1985.8387096774193), ('"SULF2"', 1980.766129032258), ('"TMSB4X"', 1958.975806451613), ('"BHLHE40"', 1735.467741935484), ('"HK2"', 1721.5725806451612), ('"IDH2"', 1708.524193548387), ('"ANXA2"', 1690.5645161290322), ('"CLDN4"', 1613.3064516129032), ('"AARS1"', 1565.782258064516), ('"TFF1"', 1561.6693548387098), ('"PFKP"', 1549.1935483870968), ('"CDKN1A"', 1449.1532258064517), ('"MALAT1"', 1448.0), ('"SLC1A5"', 1400.7016129032259), ('"ATP1A1"', 1395.0806451612902), ('"STC2"', 1253.3387096774193), ('"TRIB3"', 1205.3064516129032), ('"NDRG1"', 1203.6370967741937), ('"VEGFA"', 1190.1048387096773), ('"JUP"', 1124.7903225806451), ('"GSN"', 1099.258064516129), ('"MYH9"', 1090.3145161290322), ('"DNAJB1"', 1081.3709677419354), ('"HSPA8"', 1065.975806451613), ('"MT-CO2"', 1055.1612903225807), ('"UBB"', 1022.266129032258), ('"S100A11"', 1018.6370967741935), ('"GPRC5A"', 1017.3145161290323), ('"SCD"', 1008.8709677419355), ('"SARS1"', 1006.8709677419355), ('"HILPDA"', 987.2096774193549), ('"ELF3"', 975.483870967742), ('"DDX5"', 974.8145161290323), ('"S100A10"', 965.0725806451613), ('"MLPH"', 922.5725806451613), ('"SOX4"', 917.233870967742), ('"TPM4"', 881.9435483870968), ('"CCND1"', 881.8548387096774), ('"FOS"', 861.1935483870968), ('"MT-ND5"', 857.0564516129032), ('"MRNIP"', 839.3870967741935), ('"SLC7A5"', 836.258064516129), ('"CLK3"', 823.0483870967741), ('"KDM5B"', 822.6209677419355), ('"BAMBI"', 796.7177419354839), ('"GATA3"', 791.4354838709677), ('"TFRC"', 787.6774193548387), ('"AHNAK"', 758.3709677419355), ('"COX6C"', 741.6129032258065), ('"LMNA"', 729.0645161290323), ('"GDF15"', 718.8951612903226), ('"UGDH"', 718.8306451612904), ('"XBP1"', 713.9516129032259), ('"KDM3A"', 705.5725806451613), ('"FAM162A"', 703.5), ('"ENO2"', 693.508064516129), ('"PTP4A1"', 691.3951612903226), ('"ACTN1"', 690.2096774193549), ('"ZFP36L1"', 682.5806451612904), ('"ZYX"', 681.75), ('"TNFRSF12A"', 671.1612903225806), ('"TLE1"', 665.6935483870968), ('"GRN"', 657.9032258064516), ('"MT-CYB"', 655.6451612903226), ('"PFKFB4"', 651.8145161290323), ('"IGFBP3"', 650.75), ('"PHLDA3"', 647.741935483871), ('"TUFT1"', 639.6693548387096), ('"NQO1"', 634.3467741935484), ('"XPOT"', 629.8064516129032), ('"HSP90AA1"', 629.483870967742), ('"ARL6IP1"', 626.3225806451613), ('"KLF6"', 618.6370967741935), ('"DDIT3"', 614.1290322580645), ('"ERO1A"', 606.1612903225806), ('"DDR1"', 596.9274193548387), ('"TACSTD2"', 596.0322580645161), ('"BNIP3L"', 590.8629032258065), ('"CRIP2"', 586.9274193548387), ('"NME1-NME2"', 581.2741935483871), ('"TRIM16"', 578.4354838709677), ('"TGIF1"', 572.4193548387096), ('"IARS1"', 572.233870967742), ('"BCAS1"', 568.6532258064516), ('"PREX1"', 562.9758064516129), ('"PPM1D"', 551.491935483871), ('"JUNB"', 549.4032258064516), ('"TUBB"', 548.6693548387096), ('"ERRFI1"', 544.266129032258), ('"PPP1R15A"', 542.3870967741935), ('"SLC39A6"', 529.5322580645161), ('"TSC22D2"', 524.9193548387096), ('"FXYD3"', 518.4596774193549), ('"PTPRF"', 513.9193548387096), ('"HLA-C"', 512.9112903225806), 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452.7206675415894), ('"CCNB1"', 449.5580465174275), ('"TFRC"', 448.5261419067722), ('"LMCD1"', 447.2368231304395), ('"MBTPS1"', 446.87013855448805), ('"WARS1"', 439.3622183688267), ('"IARS1"', 436.77104398035664), ('"OVOL1"', 434.1424935369336), ('"PIM3"', 433.0404183605848), ('"CEACAM6"', 430.8186197502675), ('"TRAFD1"', 429.751638992674), ('"IGFBP5"', 422.0514356946149), ('"TXNDC5"', 416.4134189999285), ('"ID1"', 415.7196834792655), ('"PFKFB4"', 414.77337071690306), ('"UCP2"', 414.6522343827726), ('"DUSP4"', 414.1468572791273), ('"MYBL2"', 408.99404995745715), ('"ITGA5"', 408.50200482876613), ('"ANGPTL4"', 407.65458461704685), ('"BLOC1S5-TXNDC5"', 407.2515255646617), ('"PPP1R15A"', 405.60605481436284), ('"PADI2"', 405.1760104724438), ('"CITED2"', 401.81546736505175), ('"CLU"', 401.1926969114403), ('"PTPRF"', 399.8345917508339), ('"PMEPA1"', 397.0554836635109), ('"SRF"', 395.9080738305379), ('"SPTSSB"', 393.2080963916251), ('"TPX2"', 392.33879041219336), ('"ID3"', 392.14091127530946), ('"GLUL"', 388.5952206036547), ('"SLC25A25"', 387.3290206783348), ('"ID2"', 386.67326009282095), ('"AGR2"', 384.5337682468324), ('"MYADM"', 383.2856342973135), ('"MYO1B"', 381.6994085663074), ('"SEMA3C"', 381.659605175079), ('"OSER1"', 378.5093888681186), ('"QSOX1"', 376.9489850186398), ('"GFRA1"', 376.52643263328105), ('"AURKA"', 375.02665313096776), ('"TSPAN15"', 372.7862025075008), ('"TXNRD1"', 371.3594887958286), ('"TSC22D3"', 364.98345744247285), ('"EEF1A2"', 364.7341918226209), ('"ISG15"', 363.41126416009314), ('"FAM162A"', 363.4027195624241), ('"TLE1"', 363.3579877684147), ('"PRC1"', 359.2458359030623), ('"FAM83D"', 358.505218382743), ('"HMGB2"', 357.31214246348947), ('"RAP2B"', 356.61921984780497), ('"KIF22"', 354.3246775455458), ('"HLA-C"', 352.61729898898744), ('"IER2"', 351.89162224756814), ('"DSCAM-AS1"', 349.7726438323318), ('"PHGDH"', 346.9029188833945), ('"SLC38A2"', 345.64333757633756), ('"TUBB4B"', 345.32913295986253), ('"DDX41"', 342.44834625551056), ('"CEACAM5"', 341.4937413006212), ('"IRF2BP2"', 341.0061033706887), ('"KYNU"', 338.31044033374815), ('"ST3GAL1"', 335.3551161979438), ('"PLIN2"', 334.82622865641173), ('"EGLN3"', 330.53130544051794), ('"ATP9A"', 329.66656497102554), ('"IGF1R"', 329.1834778113744), ('"BNIP3L"', 328.72743879868784), ('"TGFBI"', 327.71133784876724), ('"THBS1"', 327.68023180657525), ('"MT-CO3"', 327.50927458774555), ('"LOXL2"', 323.6836404098523), ('"HSPA1B"', 320.84941519839145), ('"AEN"', 320.322187333122), ('"KIF20A"', 318.08692867033386), ('"ASS1"', 316.53076369291153), ('"HIGD1A"', 315.8744476317555), ('"P4HA2"', 315.8603032715502), ('"CHPF"', 312.9376633088937), ('"JUN"', 311.7705286101218), ('"LY6E"', 311.4379022245694), ('"LPCAT1"', 310.47800933837004), ('"N4BP3"', 310.288158670231), ('"TANC2"', 307.3896638714814), ('"PRSS23"', 307.24290839664866), ('"BAG1"', 307.0299624620736), ('"NME1-NME2"', 306.98275523478435), ('"NOL3"', 305.40824294653623), ('"IER5L"', 305.255819484473), ('"CIART"', 304.5721908622486), ('"PRSS8"', 303.4937876633975), ('"DNAJA1"', 300.5226433498997), ('"ECE1"', 299.89720712492857), ('"ACAA2"', 294.89900601342356), ('"OSR2"', 294.4091474999629), ('"EIF4EBP1"', 293.5265266708076), ('"ITPRID2"', 293.4040155465511), ('"ALDOC"', 292.7033644707813), ('"SLC6A14"', 291.87387420732637), ('"ITGB4"', 290.74610245421303), ('"ARRDC3"', 288.5698235719006), ('"PAWR"', 288.53193710689686), ('"MYLIP"', 287.584596728291), ('"CCN5"', 287.51192223766327), ('"SREBF1"', 285.6686116198818), ('"CASP14"', 282.80524798425785), ('"SPTBN1"', 282.4034797014644), ('"ELAPOR1"', 281.6142505042501), ('"EDN1"', 280.5724371229616), ('"GYS1"', 279.18780897038044), ('"ANXA1"', 278.8985608160823), ('"H4C3"', 278.22083142452203), ('"LYPD3"', 277.53451974699163), ('"TPM1"', 277.468656305718), ('"MT-ATP6"', 277.3689156020374), ('"TUBA1A"', 276.6207043887206), ('"TNFRSF21"', 276.32208045194733), ('"NR2F2"', 275.8480601851208), ('"H1-0"', 275.28549362958597), ('"EDN2"', 273.9091805357144), ('"CCN2"', 273.8412791342476), ('"SCARB1"', 273.22479454768643), ('"ANXA3"', 270.9696433187945), ('"SLC9A3R2"', 270.2382261249618), ('"SEMA4B"', 269.47270927460823), ('"PYGB"', 269.0631827812017), ('"ITGB5"', 267.9802152237677), ('"CD55"', 265.31715838193475), ('"LOX"', 264.10044002361326), ('"FOSL2"', 263.9170823023266), ('"FADS3"', 263.900974417399), ('"LAMB2"', 263.826454906286), ('"RASSF7"', 262.41156753522057), ('"IER5"', 261.63553183983254), ('"AMOTL2"', 260.98024027059375), ('"MKNK2"', 260.9285426446108), ('"DHCR7"', 260.6915458690502), ('"PCK2"', 260.5264385374851), ('"MAGED2"', 260.20144143698576), ('"MCM7"', 259.6448748854634), ('"CD59"', 259.2965892889638), ('"P4HA1"', 258.39044693221035), ('"WSB1"', 257.78081490161395), ('"DHCR24"', 257.3101864702774), ('"NFE2L2"', 256.96867783833426), ('"H2AZ1"', 256.1333709181131), ('"NR4A1"', 255.64687239907173), ('"DEGS2"', 255.21966236087542), ('"CACNG4"', 254.5928035258136), ('"GADD45A"', 254.54249479186174), ('"SLC9A3R1"', 253.2918655326897), ('"DLC1"', 249.9106280064106), ('"RAB31"', 248.1406265042037), ('"H1-2"', 247.980295825606), ('"GRAMD1A"', 246.39256540937114), ('"IFT122"', 246.34212356739272), ('"CKAP5"', 246.3277998900829), ('"PDLIM7"', 244.8273770064629), ('"SPOCK1"', 244.5468547644606), ('"BCAR1"', 243.91823029132132), ('"S100P"', 241.1060986368764), ('"LGALS1"', 240.2449907618318), ('"TOP2A"', 239.7893849223244), ('"ALCAM"', 238.79891179138804), ('"SYTL2"', 237.3509321409929), ('"LXN"', 236.37282357126574), ('"XPC"', 235.70159913940722), ('"CALM2"', 235.67518775925606), ('"NRP1"', 235.50420253688904), ('"PIM1"', 234.2736256066076), ('"NUPR1"', 233.47482582888918), ('"ETS2"', 233.15568342156172), ('"HMOX1"', 231.9665548299244), ('"MAGED1"', 231.2992323958241), ('"H2AC6"', 230.24456660492206), ('"RND3"', 230.08255211859216), ('"GSTM3"', 227.96567980084208), ('"GPX3"', 226.54951467395247), ('"PDLIM5"', 225.44320584956503), ('"SCUBE2"', 225.15960886083812), ('"SIPA1L2"', 223.6284505833434), ('"ADGRG1"', 223.48468670752735), ('"TES"', 222.4527295620088), ('"RGS16"', 221.6029916688878), ('"CCN1"', 220.31017818785736), ('"NCAPD2"', 220.1130308008827), ('"MT-RNR1"', 219.2764375055123), ('"VCL"', 218.25828444908626), ('"PLOD2"', 217.16119479920775), ('"DAAM1"', 216.98860994301967), ('"IRF9"', 215.59901412989916), ('"GADD45G"', 214.64796761208805), ('"PINK1"', 213.9003954259748), ('"OBSL1"', 213.4813134153677), ('"GRB10"', 213.3170182507099), ('"PLOD1"', 212.9340144402454), ('"PTGER4"', 212.14641955666067), ('"TOB1"', 210.65201503857296), ('"CXXC5"', 210.55788920141822), ('"NR3C1"', 210.4467361204999), ('"FZR1"', 209.75716402533854), ('"LAMA3"', 209.18720150636022), ('"TUBA1C"', 207.91060710825894), ('"TMEM45A"', 206.70593229729093), ('"AVPI1"', 205.89586000338997), ('"SH3BP4"', 205.785903757172), ('"PEBP1"', 205.35715850482026), ('"PDXK"', 205.00756176365297), ('"NME1"', 204.59518537037346), ('"BTG1"', 203.12493829812053), ('"LRP4"', 202.47735851380128), ('"PALLD"', 201.9132254940861), ('"DUSP2"', 201.253708226071), ('"SHB"', 201.10905455178235), ('"SMIM14"', 200.5681112985468), ('"FHL2"', 199.1303297124326), ('"NECTIN2"', 196.897046927846), ('"TSKU"', 195.83293079066496), ('"TUBA4A"', 194.46256441491522), ('"DEDD2"', 193.06531071483613), ('"ZNF292"', 192.693480785864), ('"HLA-B"', 192.6668166270877), ('"ID4"', 191.3423935059433), ('"PLAUR"', 191.27541938483495), ('"MCM2"', 191.27359167917723), ('"WEE1"', 190.88641490072914), ('"CSRNP1"', 190.68300275586907), ('"TBC1D9"', 190.5393362464173), ('"INSIG1"', 190.13937494378325), ('"IFI6"', 189.64283655077077), ('"CEBPG"', 189.5586802201031), ('"TUBB3"', 188.19777305635864), ('"MGLL"', 188.15524584278515), ('"GADD45B"', 188.1251311629715), ('"ARPC1B"', 186.9285301224013), ('"TAP1"', 186.16785147309554), ('"CHD2"', 186.02327400360468), ('"FBP1"', 184.92653381402852), ('"IFFO2"', 184.01324155024122), ('"PXDN"', 183.42598235925695), ('"ARFGEF3"', 183.08300746190392), ('"CDH3"', 182.898327825958), ('"ISOC1"', 182.68829351798374), ('"OAS1"', 182.49393952193162), ('"BCL6"', 181.6250946137221), ('"GTPBP2"', 181.12644300495018), ('"KIF2C"', 179.1111565091571), ('"BMP4"', 178.99003967734598), ('"ANLN"', 178.28407716028914), ('"FAM102A"', 178.2453018211015), ('"IFRD1"', 177.77363172262372), ('"AKAP12"', 177.01444727014544), ('"EPAS1"', 176.72698034971918), ('"KCTD11"', 176.0652004957159), ('"GBE1"', 176.06185043054037), ('"SLC25A39"', 175.38159886084873), ('"S100A16"', 175.19700705158226), ('"DYNC2I2"', 174.77434404084283), ('"MYC"', 174.40174067895344), ('"CYBA"', 174.15442167373465), ('"TSPYL2"', 173.8872750425264), ('"LDLR"', 172.929018150936), ('"IRF6"', 172.8335813048081), ('"DAPK3"', 172.55645795737607), ('"ANKZF1"', 172.43691876163382), ('"IFI27"', 172.13586751268022), ('"TRIB1"', 172.0411705722954), ('"RHOBTB3"', 171.11228355995834), ('"PYGL"', 170.5251148024528), ('"FUT11"', 169.45372865551917), ('"INHA"', 169.25193396782473), ('"SEMA3B"', 169.12819665958696), ('"INPP4B"', 169.0121145740848), ('"APBB2"', 168.75461101411744), ('"BMP7"', 168.6499988784138), ('"ATP1B1"', 168.54833055386248), ('"CCNB2"', 168.41498721988134), ('"STC1"', 168.27632664333962), ('"SERPINE1"', 168.07894845493402), ('"HNRNPA2B1"', 168.06707617151042), ('"STARD10"', 167.06422561771973), ('"FKBP4"', 166.81298586386885), ('"MCM3"', 166.04685589616838), ('"CAPN2"', 164.71849560902055), ('"PIK3IP1"', 164.56811050448692), ('"NOTCH1"', 164.55479737811373), ('"SLC12A2"', 164.4841515964673), ('"TMEM64"', 163.07971560000445), ('"TSPAN1"', 162.9741015097684), ('"HES1"', 162.9117970132441), ('"TK1"', 162.45209485729862), ('"ADAM15"', 162.4063188981375), ('"RACGAP1"', 162.0941442651013), ('"SPAG5"', 161.47903219604672), ('"ZWINT"', 160.958263987499), ('"SLC6A6"', 159.86634366354073), ('"CA12"', 159.68406779638013), ('"ERBB3"', 159.59957002170964), ('"TMEM106C"', 159.47469888043528), ('"GREB1"', 159.38349027075876), ('"ZNF592"', 158.9160806188448), ('"KLF3"', 158.8191797758491), ('"CABLES1"', 158.8180626564995), ('"CA9"', 158.60535386400014), ('"LGALS3BP"', 158.3982538375276), ('"TRIM16L"', 157.2147915742841), ('"ABCG2"', 156.65479361965856), ('"SAPCD2"', 156.4783150689203), ('"FOXO1"', 156.3876238913199), ('"NOTCH2"', 155.8369472997943), ('"EGR1"', 155.75627429217576), ('"OSGIN1"', 155.73093962762815), ('"CAV1"', 155.5975923321924), ('"PAM"', 154.5377774708101), ('"NECTIN4"', 154.483482210553), ('"LPP"', 154.19442084720149), ('"LAMC2"', 153.96381654682648), ('"VASN"', 153.55529501047508), ('"PTTG1"', 153.36198178189835), ('"PODXL"', 153.2636798887446), ('"NUP188"', 153.21313291135306), ('"ITPKC"', 153.19785810096803), ('"CA2"', 152.85483154918833), ('"SMTN"', 152.0946414680133), ('"GPAA1"', 151.53899781888748), ('"CRABP2"', 151.18127138027137), ('"NINJ1"', 151.02593183819482), ('"NUSAP1"', 150.99904799938895), ('"SERPINA3"', 150.7176425071609), ('"SLC25A29"', 150.71717420752512), ('"PRMT6"', 149.02805023249), ('"DSG2"', 148.88875620090562), ('"PIEZO1"', 148.776401082342), ('"RRM2"', 148.77108707450927), ('"STMN1"', 148.35917849316948), ('"ELP2"', 148.07171712387057), ('"SOX9"', 146.53880295350592), ('"GOLM1"', 145.298293923748), ('"PDK1"', 144.08892877031818), ('"UBE2S"', 143.9138240827807), ('"SNAI2"', 143.7805471026969), ('"ST6GALNAC2"', 143.3648600047643), ('"TROAP"', 142.70379807047829), ('"H3C4"', 142.5345679772105), ('"ABCC1"', 142.12767533080378), ('"PCDH1"', 141.88574781859327), ('"KLHL24"', 141.86203996084208), ('"SH3BP5"', 141.06078487715848), ('"MTHFD1"', 141.0395416466391), ('"RHOV"', 140.99642851291043), ('"H2AJ"', 140.73856272173148), ('"DHRS3"', 140.7140197448024), ('"CYFIP2"', 140.4011057494659), ('"SMAD3"', 139.80982057678975), ('"GPSM2"', 139.26410974586997), ('"NFIL3"', 138.6979283520023), ('"DBN1"', 138.5000260367084), ('"LYPD6B"', 137.8817124945893), ('"CD44"', 137.78622380234137), ('"CLIC3"', 136.21918010154582), ('"ZNF84"', 136.11779539206862), ('"PRR11"', 135.8069509663006), ('"MCM4"', 135.78203132768073), ('"BSCL2"', 135.75244647653324), ('"DLL1"', 135.7460147744456)]
520

We can notice that the first three genes are the same in both cases: GDPH, ACTG1 and ALDOA

In [ ]:
normoxia_samples_summary = normoxia_samples.describe()

normo_genes = normoxia_samples_summary.loc['mean'][normoxia_samples_summary.loc['mean'] > 92.814]
normo_genes_sorted = sorted(normo_genes.items(), key=lambda x: x[1], reverse=True)
print(normo_genes_sorted)
print(len(normo_genes_sorted)) # 538 genes overcome the mean threshold

normo_genes_std = normoxia_samples_summary.loc['std'][normoxia_samples_summary.loc['std'] > 75.526]
normo_genes_std_sorted = sorted(normo_genes_std.items(), key=lambda x: x[1], reverse=True)
print(normo_genes_std_sorted)
print(len(normo_genes_std_sorted)) ## 642 genes overcome the mean std threshold

[('"MT-CO1"', 8972.230158730159), ('"GAPDH"', 8623.079365079366), ('"ALDOA"', 5280.253968253968), ('"ACTG1"', 5217.81746031746), ('"MT-CYB"', 5064.5317460317465), ('"MT-CO2"', 4927.166666666667), ('"SULF2"', 3915.095238095238), ('"MT-ND4"', 3458.222222222222), ('"FTH1"', 3254.8650793650795), ('"BEST1"', 2862.095238095238), ('"CTSD"', 2753.2936507936506), ('"MT-CO3"', 2732.746031746032), ('"MT-RNR2"', 2682.1507936507937), ('"DDX5"', 2410.15873015873), ('"XBP1"', 2162.063492063492), ('"TMSB4X"', 2132.9841269841268), ('"TUBA1B"', 2093.404761904762), ('"SLC9A3R1"', 2052.5873015873017), ('"GATA3"', 1894.6825396825398), ('"SLC39A6"', 1805.388888888889), ('"SCD"', 1784.6746031746031), ('"COX6C"', 1780.7142857142858), ('"FTL"', 1736.920634920635), ('"HSPA5"', 1698.126984126984), ('"MT-ATP6"', 1669.9444444444443), ('"MT-ND5"', 1650.2222222222222), ('"FOS"', 1635.047619047619), ('"UBC"', 1625.4761904761904), ('"HSPA8"', 1606.873015873016), ('"HSPB1"', 1583.3412698412699), ('"NME1-NME2"', 1551.920634920635), ('"IDH2"', 1475.373015873016), ('"UBB"', 1473.3809523809523), ('"TFF1"', 1454.3015873015872), ('"GFRA1"', 1425.9285714285713), ('"NCOA3"', 1394.7539682539682), ('"PGK1"', 1379.4603174603174), ('"TUBB4B"', 1207.5714285714287), ('"CLDN4"', 1206.3333333333333), ('"DHCR7"', 1181.4603174603174), ('"FLNA"', 1146.2777777777778), ('"SLC1A5"', 1128.626984126984), ('"LDHA"', 1116.484126984127), ('"ATP1A1"', 1083.3095238095239), ('"TUBB"', 1077.8015873015872), ('"PSAP"', 1077.357142857143), ('"KPNA2"', 1075.7857142857142), ('"LMNA"', 1047.9761904761904), ('"GPI"', 1013.5952380952381), ('"MCM7"', 1003.6031746031746), ('"DYNC2I2"', 1000.6666666666666), ('"GRN"', 969.6111111111111), ('"HNRNPA2B1"', 968.0238095238095), ('"H2AZ1"', 950.2777777777778), ('"GLUL"', 934.0952380952381), ('"SLC25A39"', 927.8015873015873), ('"NME1"', 926.8968253968254), ('"CCND1"', 921.968253968254), ('"S100A11"', 915.3174603174604), ('"DSCAM-AS1"', 858.4206349206349), ('"PEBP1"', 843.6746031746031), ('"EMP2"', 834.6111111111111), ('"NR4A1"', 830.2936507936508), ('"NECTIN2"', 808.1269841269841), ('"JUP"', 791.936507936508), ('"GSTM3"', 782.5396825396825), ('"TMSB10"', 771.2777777777778), ('"ANXA2"', 763.2619047619048), ('"TMEM64"', 757.8809523809524), ('"ARPC1B"', 757.2460317460317), ('"DUSP1"', 753.6746031746031), ('"CRIP2"', 742.7539682539683), ('"FKBP4"', 739.4603174603175), ('"SQSTM1"', 728.8809523809524), ('"UGDH"', 718.3730158730159), ('"STARD10"', 717.0952380952381), ('"ARL6IP1"', 712.1587301587301), ('"FBP1"', 709.452380952381), ('"GSN"', 704.6825396825396), ('"KIF22"', 695.7301587301587), ('"AARS1"', 691.8095238095239), ('"NQO1"', 691.1587301587301), ('"SLC3A2"', 679.1190476190476), ('"MYBL2"', 648.3174603174604), ('"ACTN1"', 641.3253968253969), ('"PLK2"', 639.5), ('"HSP90AA1"', 636.1428571428571), ('"DHCR24"', 627.4126984126984), ('"TACSTD2"', 607.2063492063492), ('"THBS1"', 606.7142857142857), ('"CALM2"', 606.5873015873016), ('"MAGED2"', 591.1428571428571), ('"CYBA"', 574.6984126984127), ('"ACKR3"', 571.7380952380952), ('"CRABP2"', 568.3174603174604), ('"S100A10"', 568.0555555555555), ('"TK1"', 567.531746031746), ('"AGR2"', 559.6349206349206), ('"MALAT1"', 558.3888888888889), ('"DNAJB1"', 542.8968253968254), ('"TOB1"', 539.5555555555555), ('"PDXK"', 535.8412698412699), ('"MBTPS1"', 535.3809523809524), ('"IER2"', 532.7063492063492), ('"DSP"', 514.4920634920635), ('"CD9"', 510.484126984127), ('"SARS1"', 509.25396825396825), ('"CXXC5"', 491.07936507936506), ('"GPAA1"', 489.05555555555554), ('"MTHFD1"', 475.0), ('"MT-RNR1"', 474.4047619047619), ('"LY6E"', 473.1507936507937), ('"SPTSSB"', 470.76984126984127), ('"FDPS"', 458.8492063492063), ('"STMN1"', 456.5), ('"ISOC1"', 454.26190476190476), ('"PRMT6"', 451.7142857142857), ('"MTATP6P1"', 449.7936507936508), ('"MCM2"', 447.5079365079365), ('"MCM3"', 447.08730158730157), ('"MLPH"', 446.9920634920635), ('"BMP7"', 443.42063492063494), ('"TPD52L1"', 442.6111111111111), ('"TPM4"', 441.63492063492066), ('"H2AJ"', 441.484126984127), ('"FLNB"', 435.6904761904762), ('"CLU"', 432.07936507936506), ('"PTPRF"', 429.46031746031747), ('"NINJ1"', 429.35714285714283), ('"IARS1"', 425.04761904761904), ('"LDOC1"', 419.484126984127), ('"NUP188"', 415.8253968253968), ('"GREB1"', 412.9920634920635), ('"BSCL2"', 412.531746031746), ('"UCP2"', 411.031746031746), ('"SYTL2"', 408.3015873015873), ('"ELP2"', 402.13492063492066), ('"CA12"', 400.35714285714283), ('"EEF1A2"', 395.8015873015873), ('"IFITM3"', 394.05555555555554), ('"APEH"', 393.14285714285717), ('"SREBF1"', 388.8095238095238), ('"MAGED1"', 386.63492063492066), ('"INSIG1"', 385.968253968254), ('"PFKP"', 384.8809523809524), ('"MCM4"', 374.72222222222223), ('"LDLR"', 373.23809523809524), ('"TUBA1C"', 370.8968253968254), ('"PREX1"', 368.8015873015873), ('"SELENBP1"', 365.42063492063494), ('"AHNAK"', 365.13492063492066), ('"CDKN1A"', 364.8968253968254), ('"TFRC"', 364.6507936507937), ('"PPM1D"', 363.468253968254), ('"DNAJA1"', 363.3015873015873), ('"HMGB2"', 361.3095238095238), ('"PRSS23"', 357.36507936507934), ('"CCNB1"', 355.6666666666667), ('"IPO4"', 355.1190476190476), ('"PRC1"', 354.6031746031746), ('"ZWINT"', 354.3888888888889), ('"CBX5"', 354.05555555555554), ('"FDFT1"', 353.8888888888889), ('"DDIT4"', 344.96031746031747), ('"SRM"', 344.72222222222223), ('"CHPF"', 344.1031746031746), ('"NFKBIA"', 343.94444444444446), ('"KDM5B"', 340.92063492063494), ('"ZDHHC12"', 339.03968253968253), ('"CDC20"', 336.5), ('"DCXR"', 333.76190476190476), ('"PSME2"', 333.1825396825397), ('"IGFBP2"', 332.54761904761904), ('"DEGS2"', 330.26984126984127), ('"TNFRSF12A"', 325.6190476190476), ('"LAMB2"', 324.26984126984127), ('"IGFBP4"', 324.14285714285717), ('"MRNIP"', 323.9047619047619), ('"EBP"', 322.5), ('"GPRC5A"', 322.42063492063494), ('"DKK1"', 322.23015873015873), ('"TXNDC5"', 321.484126984127), ('"SNRNP25"', 320.58730158730157), ('"MYH9"', 320.54761904761904), ('"TM7SF2"', 320.43650793650795), ('"BLOC1S5-TXNDC5"', 319.75396825396825), ('"PIEZO1"', 318.1111111111111), ('"ZFP36L1"', 317.4920634920635), ('"AP1B1"', 317.1190476190476), ('"JUNB"', 316.77777777777777), ('"SLC52A2"', 315.6825396825397), ('"RBM14"', 311.7936507936508), ('"ITGB5"', 310.13492063492066), ('"DDR1"', 306.56349206349205), ('"FEN1"', 306.1507936507937), ('"SEMA3C"', 305.43650793650795), ('"TMEM106C"', 304.9047619047619), ('"ELAPOR1"', 300.07142857142856), ('"H4C3"', 298.6190476190476), ('"ACLY"', 298.23809523809524), ('"TIMELESS"', 298.1111111111111), ('"LSS"', 297.015873015873), ('"CYP1B1"', 296.07936507936506), ('"SMIM14"', 293.76984126984127), ('"TUBA1A"', 293.6825396825397), ('"EFNA1"', 292.75396825396825), ('"RAB31"', 291.74603174603175), ('"SQLE"', 285.3888888888889), ('"ITGB4"', 282.7857142857143), ('"PLK1"', 279.6984126984127), ('"TUBG1"', 279.484126984127), ('"WARS1"', 279.1746031746032), ('"PTP4A1"', 276.92857142857144), ('"HES1"', 276.8492063492063), ('"CDH3"', 276.031746031746), ('"SLC38A2"', 274.5079365079365), ('"BAMBI"', 272.1904761904762), ('"MYC"', 270.56349206349205), ('"MVD"', 269.531746031746), ('"ISG15"', 268.1587301587302), ('"NR2F2"', 268.0), ('"LINC00052"', 267.6825396825397), ('"TSPAN15"', 266.63492063492066), ('"PGP"', 266.14285714285717), ('"TRIB3"', 265.13492063492066), ('"PPP1R15A"', 261.515873015873), ('"RHOBTB3"', 259.1825396825397), ('"KRT80"', 258.42063492063494), ('"LYPD3"', 258.2857142857143), ('"ID3"', 257.3968253968254), ('"ASF1B"', 256.86507936507934), ('"AMOTL2"', 256.26984126984127), ('"PRSS8"', 256.0), ('"TRAFD1"', 254.35714285714286), ('"AMIGO2"', 253.18253968253967), ('"ASS1"', 249.38095238095238), ('"DDX41"', 247.22222222222223), ('"FXYD3"', 245.22222222222223), ('"OLFM1"', 243.83333333333334), ('"MT2A"', 243.77777777777777), ('"BHLHE40"', 243.13492063492063), ('"SLC7A5"', 242.3968253968254), ('"H2BC21"', 242.3968253968254), ('"DTYMK"', 242.32539682539684), ('"S100A13"', 239.70634920634922), ('"LPCAT1"', 239.15873015873015), ('"SLC2A1"', 237.38888888888889), ('"ILVBL"', 235.4047619047619), ('"ADGRG1"', 235.1984126984127), ('"NUP210"', 234.3968253968254), ('"PIM3"', 234.3095238095238), ('"FANCI"', 233.36507936507937), ('"ANXA9"', 232.79365079365078), ('"CLK3"', 232.54761904761904), ('"MCM5"', 231.86507936507937), ('"MT-ATP8"', 231.1031746031746), ('"UBE2C"', 228.82539682539684), ('"RRM2"', 228.72222222222223), ('"CKAP5"', 225.3015873015873), ('"OSR2"', 224.3015873015873), ('"XPOT"', 224.20634920634922), ('"GPER1"', 223.0079365079365), ('"LIG1"', 222.77777777777777), ('"SLC25A10"', 222.65079365079364), ('"ZMYND8"', 217.61111111111111), ('"PYGB"', 217.1031746031746), ('"ITPK1"', 216.76984126984127), ('"UNG"', 215.57142857142858), ('"RNASEH2A"', 214.3015873015873), ('"SPAG5"', 214.1031746031746), ('"CA2"', 212.5952380952381), ('"THSD8"', 212.515873015873), ('"KLF10"', 211.0793650793651), ('"RNF26"', 210.70634920634922), ('"TPM1"', 209.81746031746033), ('"TPX2"', 208.5952380952381), ('"AMFR"', 208.55555555555554), ('"IFITM2"', 207.24603174603175), ('"ERBB3"', 207.0079365079365), ('"SLC9A3R1-AS1"', 205.83333333333334), ('"TBC1D9"', 205.22222222222223), ('"PC"', 203.84920634920636), ('"ATP9A"', 201.65873015873015), ('"RHOD"', 200.55555555555554), ('"GINS2"', 200.17460317460316), ('"ACAT2"', 200.1031746031746), ('"FNDC10"', 199.88095238095238), ('"BAG3"', 198.11111111111111), ('"PKMYT1"', 197.6984126984127), ('"NCAPD2"', 196.20634920634922), ('"AURKA"', 195.34920634920636), ('"FLOT2"', 195.34126984126985), ('"ESR1"', 193.76190476190476), ('"CD59"', 191.45238095238096), ('"TFF3"', 190.45238095238096), ('"LGALS3BP"', 189.88095238095238), ('"PHLDA1"', 189.72222222222223), ('"RFC2"', 189.66666666666666), ('"ACOT7"', 188.53968253968253), ('"MCM6"', 187.6031746031746), ('"SPDEF"', 187.26984126984127), ('"ADAM15"', 187.20634920634922), ('"FADS1"', 186.15873015873015), ('"ELF3"', 185.0793650793651), ('"MOV10"', 185.0079365079365), ('"VARS1"', 184.79365079365078), ('"ATAD2"', 183.015873015873), ('"RECQL4"', 182.92857142857142), ('"CACNG4"', 182.76190476190476), ('"NR4A1AS"', 182.43650793650792), ('"CERCAM"', 181.75396825396825), ('"KIF20A"', 181.65873015873015), ('"TSC22D3"', 181.54761904761904), ('"TMEM109"', 181.04761904761904), ('"IFI30"', 180.24603174603175), ('"NOP2"', 180.17460317460316), ('"PCK2"', 179.9047619047619), ('"STC1"', 178.61904761904762), ('"BAG1"', 177.11904761904762), ('"PDLIM7"', 176.4126984126984), ('"IFT122"', 176.0873015873016), ('"HSPB8"', 175.02380952380952), ('"KYNU"', 173.73809523809524), ('"FAM102A"', 173.62698412698413), ('"RGS16"', 173.46031746031747), ('"GADD45B"', 173.13492063492063), ('"KLF6"', 172.65079365079364), ('"NCAPG2"', 171.9920634920635), ('"INTS1"', 171.47619047619048), ('"IER5"', 171.36507936507937), ('"SFXN2"', 171.20634920634922), ('"STC2"', 170.36507936507937), ('"BOP1"', 169.29365079365078), ('"PHLDA3"', 167.06349206349208), ('"CCND3"', 166.984126984127), ('"IGF1R"', 165.36507936507937), ('"ADCY1"', 165.03174603174602), ('"KCNK6"', 164.95238095238096), ('"H2AX"', 164.84920634920636), ('"TUBD1"', 164.5793650793651), ('"SLC25A29"', 163.8015873015873), ('"DOLK"', 162.9920634920635), ('"PTTG1"', 162.9126984126984), ('"G6PC3"', 161.73015873015873), ('"PCDH10"', 160.9920634920635), ('"TUFT1"', 159.11111111111111), ('"SSH3"', 159.015873015873), ('"PFKFB3"', 157.76984126984127), ('"FASN"', 157.53174603174602), ('"CCNB2"', 156.5793650793651), ('"RAMP3"', 155.93650793650792), ('"COL18A1"', 155.015873015873), ('"BCAR1"', 154.93650793650792), ('"GYS1"', 154.70634920634922), ('"SCARB1"', 154.1031746031746), ('"TLE1"', 153.46031746031747), ('"NR4A2"', 152.84920634920636), ('"DHTKD1"', 152.8095238095238), ('"DSCAM"', 150.984126984127), ('"POLD1"', 150.64285714285714), ('"MATN2"', 150.11904761904762), ('"DNMT1"', 149.68253968253967), ('"H2AC6"', 149.61111111111111), ('"S100A16"', 149.1984126984127), ('"MYADM"', 148.84126984126985), ('"PCNA"', 148.4206349206349), ('"UBE2S"', 147.26190476190476), ('"PKP3"', 147.25396825396825), ('"MDK"', 145.76984126984127), ('"ABCC5"', 145.76190476190476), ('"RRM1"', 145.23809523809524), ('"CYB561A3"', 145.16666666666666), ('"SULT2B1"', 143.62698412698413), ('"RASD1"', 143.61904761904762), ('"CRELD2"', 141.61111111111111), ('"NT5DC2"', 141.55555555555554), ('"WFS1"', 140.9920634920635), ('"ATP2A3"', 140.4126984126984), ('"NEAT1"', 139.78571428571428), ('"TRIB1"', 139.65079365079364), ('"CELSR2"', 139.44444444444446), ('"ARMC6"', 139.35714285714286), ('"SOX4"', 139.03968253968253), ('"SLC29A1"', 138.23015873015873), ('"BIRC5"', 138.22222222222223), ('"BRIP1"', 137.92857142857142), ('"PINK1"', 137.24603174603175), ('"TSKU"', 136.9920634920635), ('"EGR1"', 136.8968253968254), ('"ISYNA1"', 136.6984126984127), ('"RAMP1"', 136.65873015873015), ('"HERPUD1"', 136.65873015873015), ('"CAPG"', 134.95238095238096), ('"SDF2L1"', 134.75396825396825), ('"IFITM1"', 134.1031746031746), ('"H1-0"', 133.95238095238096), ('"FANCA"', 133.64285714285714), ('"TELO2"', 133.13492063492063), ('"SPTBN1"', 132.54761904761904), ('"SAPCD2"', 132.1031746031746), ('"UHRF1"', 131.97619047619048), ('"MXD4"', 131.34920634920636), ('"QPRT"', 131.23809523809524), ('"AURKB"', 131.17460317460316), ('"XPC"', 129.76190476190476), ('"TGIF1"', 129.75396825396825), ('"ZFP36L2"', 129.4047619047619), ('"NDST1"', 129.07142857142858), ('"MKI67"', 128.23809523809524), ('"RRP12"', 127.96031746031746), ('"PYCR3"', 127.36507936507937), ('"FADS3"', 127.02380952380952), ('"UBE2T"', 126.46825396825396), ('"CKS2"', 125.2063492063492), ('"PPRC1"', 124.5079365079365), ('"RUSC1"', 124.41269841269842), ('"EFEMP1"', 124.2063492063492), ('"SLC37A4"', 124.11904761904762), ('"ST6GALNAC2"', 123.74603174603175), ('"RANGAP1"', 123.73809523809524), ('"HYAL2"', 123.39682539682539), ('"SLC6A14"', 123.28571428571429), ('"PXDN"', 123.11111111111111), ('"TOP2A"', 122.53174603174604), ('"FAM234B"', 122.38095238095238), ('"TUBB3"', 122.17460317460318), ('"AVPI1"', 122.07142857142857), ('"OBSL1"', 121.80952380952381), ('"PLPP2"', 121.69047619047619), ('"CYP1B1-AS1"', 121.12698412698413), ('"SH3BP5"', 120.93650793650794), ('"EFHD1"', 120.7063492063492), ('"ELOVL5"', 120.22222222222223), ('"DBN1"', 120.21428571428571), ('"HLA-C"', 120.0952380952381), ('"ECE1"', 119.61904761904762), ('"ZNF331"', 119.53174603174604), ('"CTPS1"', 119.5), ('"QSOX1"', 118.86507936507937), ('"GPX3"', 118.72222222222223), ('"KIF2C"', 118.37301587301587), ('"ANLN"', 118.05555555555556), ('"BNIP3"', 117.83333333333333), ('"CSRNP1"', 117.76190476190476), ('"ABHD14A-ACY1"', 117.34126984126983), ('"ID2"', 117.15079365079364), ('"AEN"', 116.36507936507937), ('"TROAP"', 115.4920634920635), ('"RHOV"', 115.41269841269842), ('"PHGDH"', 115.1984126984127), ('"CDCA5"', 114.82539682539682), ('"CGN"', 114.5), ('"PIK3R3"', 114.43650793650794), ('"POMGNT1"', 114.35714285714286), ('"ITPRID2"', 114.23809523809524), ('"HSPA1A"', 114.02380952380952), ('"SLC27A4"', 113.92063492063492), ('"NOL3"', 113.51587301587301), ('"TEDC1"', 112.80952380952381), ('"AREG"', 112.23809523809524), ('"EIF4EBP1"', 111.67460317460318), ('"PPP1R10"', 111.51587301587301), ('"DLG5"', 111.05555555555556), ('"THSD4"', 110.9920634920635), ('"NAGLU"', 110.96825396825396), ('"PLOD1"', 110.81746031746032), ('"PHLDA1-AS1"', 110.41269841269842), ('"NAPRT"', 109.93650793650794), ('"NFE2L2"', 109.6984126984127), ('"RMI2"', 109.2063492063492), ('"SCUBE2"', 109.17460317460318), ('"PAQR4"', 108.98412698412699), ('"CAD"', 108.21428571428571), ('"MFSD3"', 108.15873015873017), ('"REEP4"', 108.14285714285714), ('"CLIC3"', 107.69047619047619), ('"CDC25B"', 107.69047619047619), ('"MADD"', 107.46031746031746), ('"SEMA4B"', 107.30952380952381), ('"TYK2"', 107.30952380952381), ('"TTF2"', 107.2936507936508), ('"MYO1B"', 107.08730158730158), ('"ZYX"', 107.05555555555556), ('"CD55"', 106.69047619047619), ('"CCN5"', 106.61904761904762), ('"CCNF"', 106.57936507936508), ('"ATP13A2"', 106.19047619047619), ('"RFX5"', 106.13492063492063), ('"NEDD9"', 105.33333333333333), ('"FN1"', 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143.24470970036853), ('"H2AC6"', 142.9452467050078), ('"CAPG"', 142.31890146528576), ('"CACNG4"', 142.1628603297741), ('"ISYNA1"', 141.69190632305123), ('"RHOV"', 141.5715943170109), ('"NUSAP1"', 141.4123465120063), ('"TUBB3"', 140.7364674483528), ('"RFC2"', 140.55683547946003), ('"THSD4"', 139.87757481636558), ('"BAG1"', 139.158936882565), ('"PKP3"', 137.69659031409228), ('"FAM102A"', 137.4475163327142), ('"TPM1"', 136.85270334450252), ('"ANKZF1"', 136.05273604057726), ('"DTYMK"', 135.60370669654006), ('"SOX4"', 135.19499403712553), ('"PC"', 134.57821918633448), ('"PYGB"', 134.06582439175645), ('"RNASEH2A"', 133.69267862325265), ('"FADS1"', 133.57289621466853), ('"ANLN"', 133.2961998291357), ('"UBE2S"', 133.0940827277564), ('"TACC3"', 133.00401068066645), ('"ABCC5"', 132.8687429651642), ('"NEDD9"', 132.4315370295157), ('"THSD8"', 132.37968026110258), ('"IER5"', 132.11441121540696), ('"POLD1"', 132.02669210645035), ('"CSRNP1"', 131.8438730360378), ('"PXDN"', 131.2579275912718), ('"CREB3L4"', 131.03081644080092), ('"PHLDA1-AS1"', 130.99088639084903), ('"SLC9A3R1-AS1"', 130.7521472099024), ('"AVPI1"', 130.5156651790997), ('"CDCA5"', 128.98099577007955), ('"HMCN1"', 128.1376581203645), ('"BIRC5"', 127.97136485254129), ('"PDLIM7"', 126.76221959819225), ('"PINK1"', 126.57511202494346), ('"ELOVL5"', 126.01059567442029), ('"FAM83D"', 125.05188764338834), ('"H2AX"', 124.92763136858507), ('"MUC5B"', 124.9087306161064), ('"CYP1B1-AS1"', 124.62532546008354), ('"DLG5"', 124.24204155151708), ('"SLC25A29"', 124.23867480563499), ('"RET"', 124.21734152890228), ('"COL18A1"', 124.03663872433721), ('"RHOD"', 123.77576858532886), ('"MXD4"', 123.25630644865632), ('"PHLDA3"', 122.48537846007552), ('"GYS1"', 122.21443891523244), ('"MAP3K1"', 122.19737897586607), ('"ATP2A3"', 121.9705059326242), ('"PSRC1"', 121.96819387635584), ('"INTS1"', 121.89206466612754), ('"FAM234B"', 121.49415506223218), ('"HERPUD1"', 121.15181634286219), ('"XPOT"', 120.51099982725678), ('"CERCAM"', 120.4636168481047), ('"RACGAP1"', 120.23820933516508), ('"PGP"', 120.17938021379304), ('"NOP2"', 119.79376139783434), ('"SMTN"', 119.71782829929053), ('"SLC25A10"', 119.48196968314961), ('"DDX41"', 119.31470245624479), ('"MT-ATP8"', 119.1576991630892), ('"TGIF1"', 117.66485874774585), ('"SPTBN1"', 117.05285009040024), ('"COL4A5"', 116.83951522263493), ('"ID1"', 116.04943336409268), ('"SEMA3B"', 115.79475657728916), ('"SH3BP5"', 115.54841382082203), ('"MYADM"', 115.20426469178388), ('"DBN1"', 115.17128858481057), ('"ACOT7"', 115.1019479100958), ('"WARS1"', 114.52294647729457), ('"PCK2"', 113.36845618223289), ('"ADAM15"', 113.30548565433664), ('"TMTC1"', 113.0618934302435), ('"E2F1"', 112.65270496755916), ('"CKS2"', 112.6051023682545), ('"VARS1"', 112.49180005389314), ('"BUB1"', 112.01124319984412), ('"BCAR1"', 112.00626739833773), ('"FANCA"', 111.78210692490738), ('"SYT12"', 111.22723458886186), ('"ADCY1"', 110.7482143608961), ('"ATP9A"', 110.45383924144333), ('"TRIM16"', 110.28203641371316), ('"SLC35C1"', 110.10144384388397), ('"SCARB1"', 109.90256261726235), ('"MAOB"', 109.19549179600507), ('"PAQR4"', 108.8422700334376), ('"CLK3"', 108.76202330908394), ('"DUSP2"', 108.38621830039244), ('"RRM1"', 108.1960759784885), ('"CDCA8"', 108.06930639236558), ('"ALCAM"', 107.90187076360496), ('"KNSTRN"', 107.7298101734148), ('"TMPRSS4"', 107.72270456763098), ('"TUBD1"', 106.98854915734506), ('"NAPRT"', 106.54642150963086), ('"CDCA3"', 106.16513522106932), ('"N4BP3"', 105.68089494111771), ('"CCN1"', 105.4939101529803), ('"MOV10"', 105.45220688306118), ('"ACAT2"', 105.36814162659067), ('"MIR4435-2HG"', 105.35687259583307), ('"WFS1"', 105.30059798741853), ('"TMEM109"', 104.82372686699186), ('"KIF23"', 104.71303431548317), ('"NT5DC2"', 104.65322206644612), ('"CALCOCO1"', 104.63251399123973), ('"PHGDH"', 104.5227645896353), ('"MUC16"', 104.28119646661101), ('"DSCAM"', 104.12991763192629), ('"CDC45"', 104.05355763808781), ('"IRF9"', 104.03612071150296), ('"RFX5"', 103.73499723234029), ('"KCNE4"', 103.57049926238801), ('"IGF1R"', 103.53941109931836), ('"CEACAM6"', 103.42047477736006), ('"CCND3"', 102.41187307159139), ('"DNMT1"', 102.4042695042468), ('"PPRC1"', 102.30618718585859), ('"SEMA4C"', 102.0666812851845), ('"RRP12"', 101.8147651998393), ('"FLOT2"', 101.6880061913626), ('"SLC39A8"', 101.65246114651066), ('"MALRD1"', 101.62759436544752), ('"SEMA4B"', 101.60428843592888), ('"H1-0"', 101.54225580656414), ('"UBE2T"', 101.23285526017222), ('"MVP"', 100.23947484629153), ('"SLC29A1"', 100.02121076638994), ('"TSC22D2"', 99.50945109457119), ('"PLPP2"', 99.10805935226172), ('"PIMREG"', 98.90342636143471), ('"TMEM164"', 98.60394145185714), ('"ALDH3B2"', 98.55547305878692), ('"BOP1"', 98.5317871519901), ('"FNDC10"', 97.88929315449018), ('"MCM10"', 97.61510681941132), ('"CGN"', 97.6090774467211), ('"NRP1"', 97.36608092290513), ('"MUC5AC"', 97.32491679200878), ('"TLE1"', 97.18038080136553), ('"POLA2"', 97.16498193426331), ('"HSPA6"', 97.15603944171458), ('"RANGAP1"', 97.14757257462928), ('"ZYX"', 96.97433108245131), ('"HSPA1A"', 96.97252924705755), ('"TRIP6"', 96.4380745464271), ('"MX1"', 96.30412804945108), ('"GCHFR"', 96.28392702406047), ('"NDST1"', 96.02642790993973), ('"DHTKD1"', 95.77680005393493), ('"S100P"', 95.7101606787014), ('"PAPSS2"', 94.96527184788577), ('"GPX3"', 94.69543928945163), ('"KANK2"', 94.69039541646757), ('"POLE"', 94.54073716724967), ('"QPRT"', 94.53324736378657), ('"DLC1"', 94.09098636046768), ('"WDR62"', 93.87106050322431), ('"AEN"', 93.70418160783248), ('"CDCA4"', 93.49527583345913), ('"ALDOC"', 93.49291799000454), ('"QSOX1"', 92.98725531379905), ('"NBEAL2"', 92.90471684849167), ('"CDT1"', 92.8602653931642), ('"HYAL2"', 92.42770834463695), ('"PIK3R3"', 92.40627649953187), ('"L1CAM"', 92.24660427354496), ('"CLDN9"', 92.08671999805401), ('"SSH3"', 91.99382449942901), ('"DOLK"', 91.98499843185265), ('"ARMC9"', 91.91259063621433), ('"SLC2A10"', 91.7284467136991), ('"IQGAP3"', 91.50923624126868), ('"FOXM1"', 91.26482936687651), ('"OSER1"', 90.94809264385677), ('"OBSL1"', 90.80482051395417), ('"FAM102B"', 90.67602367683128), ('"SLC27A4"', 90.67388626718086), ('"APBB2"', 90.55702614445212), ('"SYTL5"', 90.29657976612579), ('"HSH2D"', 89.68814541828498), ('"ARRDC3"', 89.21065919656529), ('"ESPL1"', 88.97217720984648), ('"SDC1"', 88.64081255716043), ('"PKIB"', 88.55114278702949), ('"S100A14"', 88.18554789274066), ('"CAD"', 88.02014379836986), ('"RMI2"', 87.92372307497607), ('"JUN"', 87.8877917375783), ('"CRELD2"', 87.87682035415003), ('"FADS3"', 87.84281091000804), ('"C6orf141"', 87.59598780403746), ('"HIGD1A"', 87.58128269993011), ('"NRARP"', 86.9916620708058), ('"GALNT6"', 86.77595782901422), ('"GADD45G"', 86.45495937191805), ('"G6PC3"', 86.40994504786242), ('"CTPS1"', 86.39377292374724), ('"PFKFB4"', 86.32361555898024), ('"XPC"', 86.3208367495523), ('"HJURP"', 86.20675331701293), ('"BTG2"', 86.15556988507599), ('"CDC25B"', 85.75513645590817), ('"REEP4"', 85.74359118074906), ('"TYK2"', 85.38706827483556), ('"ELOVL2"', 85.19950555039476), ('"GEM"', 85.13282134312982), ('"CYB561A3"', 85.02204420031319), ('"BCL6"', 84.99742853253186), ('"ANXA6"', 84.93731470073342), ('"MMP17"', 84.66930029649714), ('"CCNA2"', 84.6470697230472), ('"MADD"', 84.5021799286765), ('"CKB"', 83.93913781510302), ('"ITPKC"', 83.91818994219669), ('"TELO2"', 83.82089030065029), ('"SULT2B1"', 83.75504609294742), ('"GPSM2"', 83.73314414597978), ('"ATP13A2"', 83.70765453990113), ('"DNAJC22"', 83.53467372466189), ('"ABCA3"', 83.3848846262138), ('"BTG1"', 83.30563654018304), ('"EPS8L1"', 83.0896276452255), ('"PTGES"', 83.08418985086519), ('"PRR11"', 82.22745882080382), ('"TEDC1"', 81.88603927759254), ('"TONSL"', 81.46100868825486), ('"ALDH4A1"', 80.91740841207772), ('"FANCG"', 80.57370676729613), ('"SLC37A4"', 80.45317715470108), ('"EFHD1"', 79.97410255429617), ('"CELSR1"', 79.94633160131824), ('"SFXN3"', 79.91851921980111), ('"NOL3"', 79.8832882775349), ('"ABCA12"', 79.85624226428033), ('"TRMT2A"', 79.81601541959056), ('"NELL2"', 79.65917239107968), ('"PPIF"', 79.6378872105801), ('"SGSM2"', 79.3258579911478), ('"DSG2"', 79.26583122632348), ('"CAV2"', 79.25855779742015), ('"ECE1"', 79.15936908721363), ('"NOTCH2"', 79.10658271327), ('"BNIP3L"', 78.73980982192965), ('"DIP2C"', 78.65133870040025), ('"CHTF18"', 78.6353319352785), ('"TMEM94"', 78.41916999192368), ('"CXCL12"', 78.32929472871827), ('"PRC1-AS1"', 78.32702953358792), ('"TMEM150C"', 78.10070056389654), ('"ARHGEF10L"', 77.99871427511758), ('"DBP"', 77.8408029453358), ('"ETS2"', 77.83357418133431), ('"OVOL1"', 77.44410662402694), ('"PIK3IP1"', 77.40436680188009), ('"TRIP13"', 77.03572290291436), ('"NME3"', 76.92614474956964), ('"P4HA2"', 76.7689243511445), ('"FOSL2"', 76.72329709716804), ('"FHL2"', 76.72196763287758), ('"MT1X"', 76.63771917274155), ('"FASN"', 76.57526352633062), ('"GATA3-AS1"', 76.55880652898695), ('"ARMC6"', 76.45018919905579), ('"TXNRD1"', 76.31241052410807), ('"KIAA0513"', 76.2657280162018), ('"SIGMAR1"', 76.18367987545022), ('"DUSP4"', 76.0943007272858), ('"LRP8"', 75.99187926539074)]
642

Here we can notice that the first 4 genes are the same, although in different orders: MT-CO1, GAPDH, ALDOA, ACTG1

In [ ]:
# Transpose the DataFrames
hypoxia_summary_t = hypoxia_summary.transpose()
normoxia_summary_t = normoxia_summary.transpose()

# Calculate the difference in means
diff_mean = abs(hypoxia_summary_t['mean'] - normoxia_summary_t['mean'])

# Sort genes by difference in mean in descending order
sorted_genes = diff_mean.sort_values(ascending=False)

# Get the list of genes
genes_list = sorted_genes.index.tolist()
genes_list

# Create a DataFrame from the sorted series
sorted_genes_df = sorted_genes.reset_index()

# Rename the columns
sorted_genes_df.columns = ['Gene', 'Difference']

# Display the DataFrame
sorted_genes_df[:50]
Out[ ]:
Gene Difference
0 "GAPDH" 37668.952893
1 "ACTG1" 21697.537378
2 "ALDOA" 20955.116999
3 "CYP1B1" 10400.114183
4 "FTH1" 7711.505888
5 "PGK1" 7316.789683
6 "BEST1" 6846.920891
7 "MT-CO1" 6474.568868
8 "GPI" 6124.719278
9 "UBC" 5320.701229
10 "LDHA" 4750.765873
11 "MT-CYB" 4408.886585
12 "DDIT4" 4318.628392
13 "CYP1B1-AS1" 4309.364951
14 "MT-CO2" 3872.005376
15 "CYP1A1" 3232.800179
16 "FTL" 3192.740655
17 "PFKFB3" 2697.883385
18 "MT-CO3" 2375.729903
19 "SLC2A1" 2293.482079
20 "TMSB10" 2205.593190
21 "BNIP3" 2091.755376
22 "KRT80" 1959.474526
23 "SULF2" 1934.329109
24 "DSP" 1926.701485
25 "SLC3A2" 1855.913210
26 "SLC9A3R1" 1785.143753
27 "TUBA1B" 1762.235407
28 "HK2" 1692.755120
29 "BHLHE40" 1492.332821
30 "XBP1" 1448.111879
31 "DDX5" 1435.344214
32 "MT-ATP6" 1380.299283
33 "SLC39A6" 1275.856631
34 "SQSTM1" 1261.127112
35 "NDRG1" 1185.033922
36 "GFRA1" 1179.428571
37 "PFKP" 1164.312596
38 "VEGFA" 1129.327061
39 "GATA3" 1103.247056
40 "CDKN1A" 1084.256400
41 "STC2" 1082.973630
42 "COX6C" 1039.101382
43 "NME1-NME2" 970.646441
44 "TRIB3" 940.171531
45 "NCOA3" 936.334741
46 "ANXA2" 927.302611
47 "FLNA" 925.843190
48 "DHCR7" 908.637737
49 "PSAP" 908.481567

GAPDH - As discussed earlier, GAPDH is heavily involved in glycolysis and often upregulated in hypoxia to compensate for reduced mitochondrial respiration. The large difference in expression you noted fits well with the expected biological response. ACTG1 (Actin Gamma 1) - This gene encodes a cytoskeletal protein that is crucial for cell structure and integrity. Changes in ACTG1 expression might reflect cellular restructuring in response to hypoxic stress. ALDOA (Aldolase A) - Another enzyme involved in glycolysis, facilitating the breakdown of fructose-1,6-bisphosphate into triose phosphates. Its upregulation in hypoxia is consistent with increased glycolytic flux. CYP1B1 (Cytochrome P450, Family 1, Subfamily B, Polypeptide 1) - This gene is involved in drug metabolism and the synthesis of cholesterol, steroids, and other lipids. It has been implicated in various cancers and may be involved in cellular responses to environmental stressors. FTH1 (Ferritin Heavy Chain 1) - Important for iron storage; its regulation might be linked to alterations in iron metabolism under hypoxic conditions. PGK1 (Phosphoglycerate Kinase 1) - Another glycolytic enzyme, showing expected upregulation in hypoxia to enhance anaerobic energy production. MT-CO1 - Part of the mitochondrial electron transport chain, its decreased expression in hypoxia aligns with a shift away from oxidative phosphorylation. UBC (Ubiquitin C) - Plays a role in protein degradation through the ubiquitin-proteasome system; changes in its expression may reflect altered protein turnover under stress conditions. LDHA (Lactate Dehydrogenase A) - Converts pyruvate to lactate in glycolysis, crucial under anaerobic conditions, thus its upregulation in hypoxia is expected. BNIP3 - Involved in apoptosis and mitochondrial autophagy, indicative of cellular adaptations to hypoxia-driven stress.

In [ ]:
## Now with the std
# Transpose the DataFrames
hypoxia_summary_t = hypoxia_summary.transpose()
normoxia_summary_t = normoxia_summary.transpose()

# Calculate the difference in means
diff_std = abs(hypoxia_summary_t['std'] - normoxia_summary_t['std'])

# Sort genes by difference in mean in descending order
sorted_genes_std = diff_std.sort_values(ascending=False)

# Get the list of genes
genes_list = sorted_genes_std.index.tolist()
genes_list

# Create a DataFrame from the sorted series
sorted_genes_df = sorted_genes_std.reset_index()

# Rename the columns
sorted_genes_df.columns = ['Gene', 'Difference']

# Display the DataFrame
sorted_genes_df[:50]
Out[ ]:
Gene Difference
0 "ACTG1" 14800.715599
1 "GAPDH" 14023.016930
2 "ALDOA" 10400.225956
3 "CYP1B1" 8853.811534
4 "CYP1A1" 7704.798776
5 "FTH1" 7143.964639
6 "BEST1" 6414.440060
7 "UBC" 4862.663095
8 "CYP1B1-AS1" 3708.761495
9 "GPI" 3458.815260
10 "DDIT4" 3099.682389
11 "IGFBP3" 2965.667060
12 "PGK1" 2400.180962
13 "HK2" 2264.923407
14 "SLC2A1" 2218.865640
15 "FTL" 2004.389133
16 "PFKFB3" 1996.392563
17 "TMSB10" 1870.994181
18 "LDHA" 1845.092754
19 "DNAJB1" 1644.085117
20 "SQSTM1" 1621.860081
21 "TFF1" 1578.223701
22 "BHLHE40" 1519.021812
23 "HSPB1" 1398.039260
24 "SLC7A5" 1374.109916
25 "XBP1" 1372.473622
26 "SLC3A2" 1363.650266
27 "MALAT1" 1308.904278
28 "HSPA5" 1307.356407
29 "MT-CYB" 1282.562347
30 "KRT17" 1266.323914
31 "NCOA3" 1194.876389
32 "MT-CO1" 1177.776638
33 "MT2A" 1155.720993
34 "KRT81" 1154.928458
35 "MT-ND4" 1117.249721
36 "KRT80" 1108.771806
37 "BNIP3" 1064.262254
38 "TRIB3" 1051.400043
39 "GDF15" 1044.797466
40 "COX6C" 1009.500078
41 "TUBA1B" 979.218842
42 "SLC9A3R1" 977.321908
43 "AARS1" 968.278080
44 "DSP" 927.107657
45 "NR4A1" 895.658940
46 "MT1X" 873.276680
47 "VEGFA" 862.437795
48 "ARL6IP1" 860.600026
49 "COTL1" 821.729986

Even studying the std,the results are confirmed: the genes with higher std difference between one state and the other are stil ACTG1,GAPDH and ALDOA

In [ ]:
#I want to do a clustering considering only genes: GAPDH, ALDOA and PGK1 as principal components
# Create a DataFrame with only the selected genes
selected_genes = ['"GAPDH"', '"ALDOA"', '"PGK1"']
selected_genes_df = df_MCF7_norm_filt.loc[selected_genes]

# Transpose the DataFrame
selected_genes_df = selected_genes_df.T

# Apply PCA to the selected genes
pca = PCA(n_components=3)
selected_genes_transformed = pca.fit_transform(selected_genes_df)

# Apply K-means clustering to the transformed data
kmeans = KMeans(n_clusters=2, random_state=42)
clusters = kmeans.fit_predict(selected_genes_transformed)

# Assign colors to conditions manually
conditions = selected_genes_df.index.to_series().apply(extract_condition)
color_map = {'Hypo': 'red', 'Norm': 'blue'}
colors = conditions.map(color_map)

# Create a 3D scatter plot
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(selected_genes_transformed[:, 0], selected_genes_transformed[:, 1], selected_genes_transformed[:, 2], c=colors)
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
plt.title('PCA of Selected Genes (GAPDH, ALDOA, PGK1) Colored by Condition')
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
No description has been provided for this image
In [ ]:
# now we want to do a k-means clustering with the selected genes
# Apply K-means clustering to the transformed data
kmeans = KMeans(n_clusters=2, random_state=42)
clusters = kmeans.fit_predict(selected_genes_transformed)

# Assign colors to conditions manually
conditions = selected_genes_df.index.to_series().apply(extract_condition)
###color_map = {'Hypo': 'red', 'Norm': 'blue'}
###colors = conditions.map(color_map)
cluster_colors10 = ['green' if label == 0 else 'yellow' for label in clusters]

# Create a 3D scatter plot
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(selected_genes_transformed[:, 0], selected_genes_transformed[:, 1], selected_genes_transformed[:, 2], c=cluster_colors10)
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
plt.title('K-means Clustering of Selected Genes (GAPDH, ALDOA, PGK1)')
plt.show()

#i want to see the match rate of the clustering
# Assign numeric labels based on conditions
###condition_numeric = conditions.map({'C1': 0, 'C2': 1})

# Compute the confusion matrix
###cm = confusion_matrix(condition_numeric, clusters)

# Calculate the match rate using the trace of the confusion matrix
###match_rate = np.trace(cm) / np.sum(cm)

###print(f'Match Rate: {match_rate:.2%}')

c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
No description has been provided for this image

Again, the k-means clustering splits the data almost in the same way as the actual division. Indeed, the selection of genes chosen fof the PCA shows that GAPDH, ALDOA and PGK1 are some of the most indicated genes in order to distinguish between normoxic and hypoxic MCF7 cells.

¶

2.2.9 More unsupervised learning

In [ ]:
X_MCF_SMART = df_trans_MCF_SMART.drop('TARGET', axis=1)
y_train_MCF_SMART=df_trans_MCF_SMART['TARGET']

scaler_MCF_SMART = StandardScaler()
X_scaled_MCF_SMART = scaler_MCF_SMART.fit_transform(X_MCF_SMART)
In [ ]:
pca2_MCF_SMART = PCA(n_components=2)
X_pca2_MCF_SMART = pca2_MCF_SMART.fit_transform(X_scaled_MCF_SMART)
In [ ]:
plt.figure(figsize=(12, 6))

plt.scatter(X_pca2_MCF_SMART[:,0][y_train_MCF_SMART==1], X_pca2_MCF_SMART[:,1][y_train_MCF_SMART==1], c='r')
plt.scatter(X_pca2_MCF_SMART[:,0][y_train_MCF_SMART==0], X_pca2_MCF_SMART[:,1][y_train_MCF_SMART==0], c='b')

plt.title("Clusterization PCA MCF_SMART")

plt.show()
No description has been provided for this image
In [ ]:
'''% of EXPLAINED VARIANCE'''
pca_MCF_SMART = PCA().fit(X_scaled_MCF_SMART)
In [ ]:
explained_variance_ratio_MCF_SMART = pca_MCF_SMART.explained_variance_ratio_
cumulative_variance_MCF_SMART = explained_variance_ratio_MCF_SMART.cumsum()

# Визуализация объясненной дисперсии
plt.plot(range(1, len(cumulative_variance_MCF_SMART) + 1), cumulative_variance_MCF_SMART, marker='o')
plt.xlabel("Number of components")
plt.ylabel("Cumulative proportion of Variance Explained")
plt.title("Analysis of explained variance MCF_SMART")
plt.show()
No description has been provided for this image
In [ ]:
# TSNE
tsne_model_MCF_SMART = TSNE(n_components=2) #, perplexity=10
X_tsne_MCF_SMART = tsne_model_MCF_SMART.fit_transform(X_scaled_MCF_SMART)
In [ ]:
# Visualization of results
plt.figure(figsize=(12, 6))
# plt.scatter(X_train_pca[:,0], X_train_pca[:,1], cmap='viridis')


plt.scatter(X_tsne_MCF_SMART[:,0][y_train_MCF_SMART==1], X_tsne_MCF_SMART[:,1][y_train_MCF_SMART==1], c='r')
plt.scatter(X_tsne_MCF_SMART[:,0][y_train_MCF_SMART==0], X_tsne_MCF_SMART[:,1][y_train_MCF_SMART==0], c='b')

plt.title("Clusterization TSNE MCF_SMART")

plt.show()
No description has been provided for this image

¶

2.2) HCC1806

¶

2.2.0) Importing datasets

In [ ]:
hcc_unfiltered = pd.read_csv("SmartSeq\HCC1806_SmartS_Unfiltered_Data.txt",delimiter="\ ", engine='python',index_col=0)
print("UNFILTERED dimensions:", np.shape(hcc_unfiltered))
hcc_unfiltered_summary=hcc_unfiltered.describe()
hcc_unfiltered.describe()

UNFILTERED dimensions: (23396, 243)
Out[ ]:
"output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam" ... "output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam"
count 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 ... 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000
mean 99.565695 207.678278 9.694734 150.689007 35.700504 47.088434 152.799453 135.869422 38.363908 45.512139 ... 76.361771 105.566593 54.026116 29.763806 28.905411 104.740725 35.181569 108.197940 37.279962 76.303855
std 529.532443 981.107905 65.546050 976.936548 205.885369 545.367706 864.974182 870.729740 265.062493 366.704721 ... 346.659348 536.881574 344.068304 186.721266 135.474736 444.773045 170.872090 589.082268 181.398951 369.090274
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 1.000000 0.000000 0.000000 0.000000 0.000000 2.000000 0.000000 0.000000 0.000000 1.000000
75% 51.000000 125.000000 5.000000 40.000000 22.000000 17.000000 81.000000 76.000000 22.000000 18.000000 ... 56.000000 67.000000 29.000000 18.000000 19.000000 76.000000 24.000000 68.000000 22.000000 44.000000
max 35477.000000 69068.000000 6351.000000 70206.000000 17326.000000 47442.000000 43081.000000 62813.000000 30240.000000 35450.000000 ... 19629.000000 30987.000000 21894.000000 13457.000000 11488.000000 33462.000000 15403.000000 34478.000000 10921.000000 28532.000000

8 rows × 243 columns

In [ ]:
hcc_filtered = pd.read_csv("SmartSeq\HCC1806_SmartS_Filtered_Data.txt",delimiter="\ ", engine='python',index_col=0)
print("FILTERED dimensions:", np.shape(hcc_filtered))
hcc_filtered_summary=hcc_filtered.describe()
hcc_filtered.describe()

FILTERED dimensions: (19503, 227)
Out[ ]:
"output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A9_Normoxia_S20_Aligned.sortedByCoord.out.bam" ... "output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam"
count 19503.000000 19503.000000 19503.000000 19503.000000 19503.000000 19503.000000 19503.000000 19503.000000 19503.000000 19503.000000 ... 19503.000000 19503.000000 19503.000000 19503.000000 19503.000000 19503.000000 19503.000000 19503.000000 19503.000000 19503.000000
mean 119.427883 249.107522 180.739527 42.818233 56.444393 183.264677 162.976670 46.014305 54.589961 96.803210 ... 91.583090 126.622930 64.801005 35.702302 34.670461 125.629544 42.195457 129.769010 44.715941 91.517561
std 577.934133 1069.768525 1067.470509 224.823960 596.882811 944.432350 951.367277 289.708746 401.024242 487.943421 ... 377.847391 585.760835 375.921207 203.991666 147.706909 484.448028 186.359651 643.033801 197.842998 402.529704
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 3.000000 7.000000 0.000000 1.000000 0.000000 0.000000 4.000000 0.000000 0.000000 9.000000 ... 9.000000 4.000000 1.000000 2.000000 1.000000 15.000000 3.000000 4.000000 2.000000 8.000000
75% 75.000000 179.000000 111.000000 31.000000 29.000000 126.000000 106.000000 32.000000 29.000000 78.000000 ... 77.000000 94.000000 42.000000 25.000000 27.000000 105.000000 34.000000 94.000000 32.000000 63.000000
max 35477.000000 69068.000000 70206.000000 17326.000000 47442.000000 43081.000000 62813.000000 30240.000000 35450.000000 42310.000000 ... 19629.000000 30987.000000 21894.000000 13457.000000 11488.000000 33462.000000 15403.000000 34478.000000 10921.000000 28532.000000

8 rows × 227 columns

In [ ]:
hcc_removed_elements = hcc_unfiltered[~hcc_unfiltered.index.isin(hcc_filtered.index)]
columns_to_remove = hcc_filtered.columns.intersection(hcc_removed_elements.columns)
hcc_removed_elements = hcc_unfiltered.drop(columns=columns_to_remove)
hcc_removed_elements_summary=hcc_removed_elements.describe()
print("REMOVED DATA dimensions:", np.shape(hcc_removed_elements))
hcc_removed_elements.describe()

REMOVED DATA dimensions: (23396, 16)
Out[ ]:
"output.STAR.PCRPlate1A1_Hypoxia_S97_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1B1_Hypoxia_S98_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1B4_Hypoxia_S105_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1D3_Hypoxia_S6_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1F8_Normoxia_S18_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1G6_Hypoxia_S15_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate2E12_Normoxia_S61_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate2F12_Normoxia_S62_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate2G10_Normoxia_S157_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate2H2_Hypoxia_S35_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate3D10_Normoxia_S188_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate3D11_Normoxia_S92_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate3D2_Hypoxia_S168_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate3D3_Hypoxia_S72_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate3D5_Hypoxia_S78_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4B1_Hypoxia_S221_Aligned.sortedByCoord.out.bam"
count 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000
mean 9.694734 0.019106 8.914515 149.442255 118.581980 3.163105 0.011840 0.004873 20.487434 246.116088 0.028937 0.007010 0.021799 0.014618 0.027740 9.328090
std 65.546050 1.003615 43.524520 426.779488 498.780729 25.101297 0.807311 0.125665 183.626052 1484.050714 2.331891 0.680409 1.538074 1.063188 1.334005 56.229778
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 14.000000 3.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
75% 5.000000 0.000000 4.000000 142.000000 82.000000 1.000000 0.000000 0.000000 0.000000 149.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000
max 6351.000000 140.000000 2047.000000 24384.000000 32060.000000 1304.000000 98.000000 14.000000 8437.000000 85472.000000 320.000000 103.000000 220.000000 160.000000 168.000000 4863.000000

¶

2.2.1) Statistical study of mean and STD on cells

We used same approach done on MCF7 dataset (2.1.1)

¶

2.2.1.1) Study of mean on cells

In [ ]:
hcc_unfiltered_mean_max_value = hcc_unfiltered_summary.iloc[1].max()
print("Max value in unfiltered data:", hcc_unfiltered_mean_max_value)
hcc_unfiltered_mean_min_value = hcc_unfiltered_summary.iloc[1].min()
print("Min value in unfiltered data:", hcc_unfiltered_mean_min_value)

Max value in unfiltered data: 246.11608822020858
Min value in unfiltered data: 0.004872627799623867
In [ ]:
sns.displot(hcc_unfiltered_summary.iloc[1], bins=50)
plt.xlim(0, 250) 
plt.ylim(0, 20)
plt.title("Distribution of mean in unfiltered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

In unfiltered dataset means are concentrated mostly around 0 and in the intervals [25, 60] and [70, 160]

In [ ]:
hcc_filtered_mean_max_value = hcc_filtered_summary.iloc[1].max()
print("Max value in filtered data:", hcc_filtered_mean_max_value)
hcc_filtered_mean_min_value = hcc_filtered_summary.iloc[1].min()
print("Min value in filtered data:", hcc_filtered_mean_min_value)

Max value in filtered data: 249.10752191970465
Min value in filtered data: 17.54094241911501
In [ ]:
sns.displot(hcc_filtered_summary.iloc[1], bins=50)
plt.xlim(0, 250)
plt.ylim(0,20)
plt.title("Distribution of mean in filtered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

In filtered dataset, means are distributed mostly on interval [20, 200] with peaks around[30, 50], around 75, around 130 and around 160

In [ ]:
hcc_removed_elements_mean_max_value = hcc_removed_elements_summary.iloc[1].max()
print("Max value in removed elements:", hcc_removed_elements_mean_max_value)
hcc_removed_elements_mean_min_value = hcc_removed_elements_summary.iloc[1].min()
print("Min value in removed elements:", hcc_removed_elements_mean_min_value)

Max value in removed elements: 246.11608822020858
Min value in removed elements: 0.004872627799623867
In [ ]:
sns.displot(hcc_removed_elements_summary.iloc[1], bins=50)
plt.xlim(0, 250)
plt.ylim(0,10)
plt.title("Distribution of mean in removed elements")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

In removed elements dataset almost all elements have mean around 0.

In [ ]:
# is cell with highest mean value in filtered and unfiltered data the same?

max_mean_column_filtered = hcc_filtered.mean().idxmax()
max_mean_column_unfiltered = hcc_unfiltered.mean().idxmax()

print("is cell with highest mean value in filtered and unfiltered data the same?", max_mean_column_filtered==max_mean_column_unfiltered,
      "\nmax mean column in filtered data:", max_mean_column_filtered, " and the value is:", hcc_filtered.mean().max(),
      "\nmax mean column in unfiltered data:", max_mean_column_unfiltered, " and the value is:", hcc_unfiltered.mean().max()) 

is cell with highest mean value in filtered and unfiltered data the same? False 
max mean column in filtered data: "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam"  and the value is: 249.10752191970465 
max mean column in unfiltered data: "output.STAR.PCRPlate2H2_Hypoxia_S35_Aligned.sortedByCoord.out.bam"  and the value is: 246.11608822020858
In [ ]:
# is cell with highest mean value in unfiltered contained in filtered?
print("is cell with highest mean value in unfiltered contained in filtered?")
if max_mean_column_unfiltered in hcc_filtered:
    print("yes")
else:
    print("no")

is cell with highest mean value in unfiltered contained in filtered?
no
In [ ]:
# is cell with lowest mean value in filtered and unfiltered data the same?

min_mean_column_filtered = hcc_filtered.mean().idxmin()
min_mean_column_unfiltered = hcc_unfiltered.mean().idxmin()

print("is cell with lowest mean value in filtered and unfiltered data the same?", min_mean_column_filtered==min_mean_column_unfiltered,
        "\nmin mean column in filtered data:", min_mean_column_filtered, " and the value is:", hcc_filtered.mean().min(),
        "\nmin mean column in unfiltered data:", min_mean_column_unfiltered, " and the value is:", hcc_unfiltered.mean().min())

is cell with lowest mean value in filtered and unfiltered data the same? False 
min mean column in filtered data: "output.STAR.PCRPlate3D1_Hypoxia_S161_Aligned.sortedByCoord.out.bam"  and the value is: 17.54094241911501 
min mean column in unfiltered data: "output.STAR.PCRPlate2F12_Normoxia_S62_Aligned.sortedByCoord.out.bam"  and the value is: 0.004872627799623867
In [ ]:
# is cell with lowest mean value in unfiltered contained in filtered?
print("is cell with lowest mean value in unfiltered contained in filtered?")
if min_mean_column_unfiltered in hcc_filtered:
    print("yes")
else:
    print("no")

is cell with lowest mean value in unfiltered contained in filtered?
no

¶

2.2.1.2) Study of STD

In [ ]:
hcc_unfiltered_std_max_value = hcc_unfiltered_summary.iloc[2].max()
print("max STD value in unfiltered:", hcc_unfiltered_std_max_value)
hcc_unfiltered_std_min_value = hcc_unfiltered_summary.iloc[2].min()
print("min TD value in unfiltered:", hcc_unfiltered_std_min_value)

max STD value in unfiltered: 1794.9847811738643
min TD value in unfiltered: 0.1256646395877129
In [ ]:
sns.displot(hcc_unfiltered_summary.iloc[2], bins=100)
plt.xlim(0, 1750)
plt.ylim(0,15)
plt.title("Distribution of STD in unfiltered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

STD in unfiltered is distributed mainly around 0 and in the interval [150, 800] with some peaks around 180, 250, 450 and 600.

In [ ]:
hcc_filtered_std_max_value = hcc_filtered_summary.iloc[2].max()
print("max STD value in filtered:", hcc_filtered_std_max_value)
hcc_filtered_std_min_value = hcc_filtered_summary.iloc[2].min()
print("min STD value in filtered:", hcc_unfiltered_std_min_value)

max STD value in filtered: 1964.1905749569585
min STD value in filtered: 0.1256646395877129
In [ ]:
sns.displot(hcc_filtered_summary.iloc[2], bins=100)
plt.xlim(0, 1750)
plt.ylim(0,15)
plt.title("Distribution of STD in filtered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

In filtered dataset STD is mostly distributed in interval [0, 800] with peaks in intervall [200, 300], and around 700.

In [ ]:
hcc_removed_elements_std_max_value = hcc_removed_elements_summary.iloc[2].max()
print("max STD value in removed elements:", hcc_removed_elements_std_max_value)
hcc_removed_elements_std_min_value = hcc_removed_elements_summary.iloc[2].min()
print("min STD value in removed elements:", hcc_removed_elements_std_min_value)

max STD value in removed elements: 1484.0507137548668
min STD value in removed elements: 0.1256646395877129
In [ ]:
sns.displot(hcc_removed_elements_summary.iloc[2], bins=100)
plt.xlim(0, 1750)
plt.ylim(0,10)
plt.title("Distribution of STD in removed elements")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Almost all elements in removed elements dataframe have STD equal to 0

In [ ]:
# is cell with highest std value in filtered and unfiltered data the same?

max_std_column_filtered = hcc_filtered.std().idxmax()
max_std_column_unfiltered = hcc_unfiltered.std().idxmax()

print("is cell with max std value in filtered and unfiltered data the same?", max_std_column_filtered==max_std_column_unfiltered,
        "\nmin std column in filtered data:", max_std_column_filtered, " and the value is:", hcc_filtered.std().max(),
        "\nmin std column in unfiltered data:", max_std_column_unfiltered, " and the value is:", hcc_unfiltered.std().max())

is cell with max std value in filtered and unfiltered data the same? True 
min std column in filtered data: "output.STAR.PCRPlate1G8_Normoxia_S19_Aligned.sortedByCoord.out.bam"  and the value is: 1964.1905749569585 
min std column in unfiltered data: "output.STAR.PCRPlate1G8_Normoxia_S19_Aligned.sortedByCoord.out.bam"  and the value is: 1794.9847811738641
In [ ]:
# is cell with max std value in unfiltered contained in filtered?
print("is cell with max std value in unfiltered contained in filtered?")
if max_std_column_unfiltered in hcc_filtered:
    print("yes")
else:
    print("no")

is cell with max std value in unfiltered contained in filtered?
yes
In [ ]:
# is cell with lowest std value in filtered and unfiltered data the same?

min_std_column_filtered = hcc_filtered.std().idxmin()
min_std_column_unfiltered = hcc_unfiltered.std().idxmin()

print("is cell with lowest std value in filtered and unfiltered data the same?", min_std_column_filtered==min_std_column_unfiltered,
        "\nmin std column in filtered data:", min_std_column_filtered, " and the value is:", hcc_filtered.std().min(),
        "\nmin std column in unfiltered data:", min_std_column_unfiltered, " and the value is:", hcc_unfiltered.std().min())

is cell with lowest std value in filtered and unfiltered data the same? False 
min std column in filtered data: "output.STAR.PCRPlate3F3_Hypoxia_S170_Aligned.sortedByCoord.out.bam"  and the value is: 88.62128925222184 
min std column in unfiltered data: "output.STAR.PCRPlate2F12_Normoxia_S62_Aligned.sortedByCoord.out.bam"  and the value is: 0.1256646395877129
In [ ]:
# is cell with lowest std value in unfiltered contained in filtered?
print("is cell with lowest std value in unfiltered contained in filtered?")
if min_std_column_unfiltered in hcc_filtered:
    print("yes")
else:
    print("no")

is cell with lowest std value in unfiltered contained in filtered?
no

¶

2.2.1.3) Details of distribution of mean

In [ ]:
hcc_unfiltered_summary.iloc[1].describe()

count    243.000000
mean      86.010698
std       50.087964
min        0.004873
25%       42.360339
50%       88.376004
75%      125.029129
max      246.116088
Name: mean, dtype: float64

In the unfiltered dataset the mean value for means is 86.01, almost equal to the median value (88.37), suggesting that the distribution is symmetric.

Moreover, given the maximum value equal to 246 and minimum value 0, the expected 25% qualtile, 50% qualtile and 75 quantile in case of costant distribution function would have been 61.5, 123 and 184, while they are 42.4, 88.3 and 125, suggesting that the majority of datas have low mean and distribution is shifted toward 0.

In [ ]:
hcc_filtered_summary.iloc[1].describe()

count    227.000000
mean     107.439569
std       55.590688
min       17.540942
25%       52.710173
50%      110.598728
75%      152.016254
max      249.107522
Name: mean, dtype: float64

In the filtered dataset the mean value for means is 107.4, and angain is close to median value (110.6), suggesting that the distribution is symmetric.

Moreover, given the maximum value equal to 249 and minimum value 10, the expected 25% qualtile, 50% qualtile and 75 quantile in case of costant distribution function would have been 69.75, 129.5 and 189.25, while they are 52, 110.3 and 152, suggesting again that the majority of datas have mean between the 25% quantile and the 50% quantile.

Furthermore, passing from funfiltered to filtered dataset, STD increases, so the distribution of the mean is more spread

In [ ]:
hcc_removed_elements_summary.iloc[1].describe()

count     16.000000
mean      35.366508
std       71.946914
min        0.004873
25%        0.017984
50%        1.596021
75%       12.392909
max      246.116088
Name: mean, dtype: float64

In removed elements the mean is much larger than the median, saying that there are just few elements that have high mean while majority of elements have low mean.

Moreover, the 25% quantile (0.017984) is very close to minimum value (0.004873) and the 50% one is 1.596021, so, as further confirm of what stated before, vast majority of elements have low mean.

¶

2.2.1.4) Details of distribution of STD

In [ ]:
hcc_unfiltered_summary.iloc[2].describe()

count     243.000000
mean      478.838710
std       324.107809
min         0.125665
25%       227.812122
50%       444.773045
75%       641.865658
max      1794.984781
Name: std, dtype: float64
In [ ]:
hcc_filtered_summary.iloc[2].describe()

count     227.000000
mean      546.126224
std       338.318040
min        88.621289
25%       277.861442
50%       512.384830
75%       704.588843
max      1964.190575
Name: std, dtype: float64

Passing from unfiltered to filtered dataset mean of STD increases, implying that the distribution is more spread

In [ ]:
hcc_removed_elements_summary.iloc[2].describe()

count      16.000000
mean      174.532674
std       382.069532
min         0.125665
25%         1.048295
50%        13.716594
75%        95.066050
max      1484.050714
Name: std, dtype: float64

¶

2.2.2) Statistical study of mean and STD on genes

We applied same technique done before (see 2.1.2)

¶

2.2.2.1) Study of mean

In [ ]:
hcc_unfiltered_transposed = hcc_unfiltered.T
print("UNFILTERED TRANSPOSED dimensions:", np.shape(hcc_unfiltered_transposed))
hcc_unfiltered_transposed_summary=hcc_unfiltered_transposed.describe()
hcc_unfiltered_transposed.describe()

UNFILTERED TRANSPOSED dimensions: (243, 23396)
Out[ ]:
"WASH7P" "CICP27" "DDX11L17" "WASH9P" "OR4F29" "MTND1P23" "MTND2P28" "MTCO1P12" "MTCO2P12" "MTATP8P1" ... "MT-TH" "MT-TS2" "MT-TL2" "MT-ND5" "MT-ND6" "MT-TE" "MT-CYB" "MT-TT" "MT-TP" "MAFIP"
count 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 ... 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000
mean 0.045267 0.119342 0.469136 0.255144 0.127572 117.930041 28.427984 904.308642 1.403292 0.378601 ... 10.358025 5.930041 13.493827 2290.213992 386.901235 18.246914 2163.588477 20.613169 46.444444 3.897119
std 0.318195 0.594531 1.455282 0.818639 0.440910 103.038022 26.062662 654.520308 1.735003 0.747361 ... 10.910199 7.040559 11.803967 1726.423259 309.276105 54.076514 1730.393947 22.224590 47.684223 4.736193
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 46.500000 8.000000 390.500000 0.000000 0.000000 ... 2.000000 1.000000 4.000000 918.000000 138.500000 4.000000 947.500000 5.000000 14.000000 0.000000
50% 0.000000 0.000000 0.000000 0.000000 0.000000 94.000000 22.000000 790.000000 1.000000 0.000000 ... 7.000000 4.000000 10.000000 1848.000000 320.000000 11.000000 1774.000000 14.000000 38.000000 2.000000
75% 0.000000 0.000000 0.000000 0.000000 0.000000 157.000000 42.000000 1208.000000 2.000000 1.000000 ... 14.000000 7.500000 20.000000 3172.000000 528.000000 20.000000 2927.000000 30.500000 64.500000 6.000000
max 3.000000 5.000000 12.000000 6.000000 4.000000 694.000000 120.000000 3569.000000 12.000000 4.000000 ... 52.000000 43.000000 57.000000 8972.000000 1439.000000 804.000000 11383.000000 154.000000 409.000000 24.000000

8 rows × 23396 columns

In [ ]:
hcc_filtered_transposed = hcc_filtered.T
print("FILTERED TRANSPOSED dimensions:", np.shape(hcc_filtered_transposed))
hcc_filtered_transposed_summary=hcc_filtered_transposed.describe()
hcc_filtered_transposed.describe()

FILTERED TRANSPOSED dimensions: (227, 19503)
"CICP27" "DDX11L17" "WASH9P" "OR4F29" "MTND1P23" "MTND2P28" "MTCO1P12" "MTCO2P12" "MTATP8P1" "MTATP6P1" ... "MT-TH" "MT-TS2" "MT-TL2" "MT-ND5" "MT-ND6" "MT-TE" "MT-CYB" "MT-TT" "MT-TP" "MAFIP"
count 227.000000 227.000000 227.00000 227.000000 227.000000 227.000000 227.000000 227.000000 227.000000 227.000000 ... 227.000000 227.000000 227.000000 227.000000 227.000000 227.000000 227.000000 227.000000 227.000000 227.000000
mean 0.101322 0.488987 0.23348 0.123348 121.867841 30.035242 952.537445 1.453744 0.405286 532.171806 ... 10.691630 6.193833 14.281938 2415.079295 406.603524 15.665198 2268.911894 21.682819 47.400881 4.057269
std 0.574801 1.491454 0.74259 0.443968 102.526406 25.907060 635.213609 1.747646 0.766308 387.720513 ... 10.744501 6.993509 11.758626 1690.605545 304.982898 19.386675 1700.731948 22.411593 41.691796 4.769119
min 0.000000 0.000000 0.00000 0.000000 1.000000 0.000000 5.000000 0.000000 0.000000 4.000000 ... 0.000000 0.000000 0.000000 18.000000 5.000000 0.000000 83.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.00000 0.000000 49.500000 9.000000 473.500000 0.000000 0.000000 238.500000 ... 3.000000 1.000000 5.000000 1067.500000 163.000000 4.000000 1008.500000 6.000000 16.000000 0.000000
50% 0.000000 0.000000 0.00000 0.000000 99.000000 25.000000 826.000000 1.000000 0.000000 466.000000 ... 8.000000 4.000000 11.000000 2002.000000 329.000000 11.000000 1837.000000 15.000000 38.000000 2.000000
75% 0.000000 0.000000 0.00000 0.000000 158.000000 42.500000 1247.500000 2.000000 1.000000 696.000000 ... 14.000000 8.000000 21.000000 3324.500000 549.500000 20.500000 3046.000000 31.000000 65.500000 6.500000
max 5.000000 12.000000 6.00000 4.000000 694.000000 120.000000 3569.000000 12.000000 4.000000 2358.000000 ... 52.000000 43.000000 57.000000 8972.000000 1439.000000 212.000000 11383.000000 154.000000 289.000000 24.000000

8 rows × 19503 columns

In [ ]:
hcc_removed_elements_transposed=hcc_removed_elements.T
print("REMOVED DATA dimensions:", np.shape(hcc_removed_elements_transposed))
hcc_removed_elements_transposed_summary=hcc_removed_elements_transposed.describe()
hcc_removed_elements_transposed.describe()

REMOVED DATA dimensions: (16, 23396)
"WASH7P" "CICP27" "DDX11L17" "WASH9P" "OR4F29" "MTND1P23" "MTND2P28" "MTCO1P12" "MTCO2P12" "MTATP8P1" ... "MT-TH" "MT-TS2" "MT-TL2" "MT-ND5" "MT-ND6" "MT-TE" "MT-CYB" "MT-TT" "MT-TP" "MAFIP"
count 16.000 16.000000 16.0000 16.000000 16.000000 16.000000 16.000000 16.000000 16.000000 16.0 ... 16.000000 16.000000 16.000000 16.000000 16.000000 16.000000 16.000000 16.000000 16.000000 16.000000
mean 0.125 0.375000 0.1875 0.562500 0.187500 62.062500 5.625000 220.062500 0.687500 0.0 ... 5.625000 2.187500 2.312500 518.687500 107.375000 54.875000 669.312500 5.437500 32.875000 1.625000
std 0.500 0.806226 0.7500 1.547848 0.403113 96.670898 15.894968 545.452286 1.400893 0.0 ... 12.467959 6.843184 4.686417 1199.906647 229.222708 199.958954 1475.697788 11.752837 101.412606 3.649201
min 0.000 0.000000 0.0000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000 0.000000 0.0000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 0.000 0.000000 0.0000 0.000000 0.000000 3.000000 0.000000 4.500000 0.000000 0.0 ... 0.000000 0.000000 0.000000 1.000000 6.000000 0.000000 12.500000 0.000000 0.000000 0.000000
75% 0.000 0.000000 0.0000 0.000000 0.000000 115.000000 2.250000 85.750000 1.000000 0.0 ... 1.000000 0.000000 2.250000 234.500000 50.500000 8.250000 445.750000 2.500000 14.000000 0.250000
max 2.000 2.000000 3.0000 6.000000 1.000000 282.000000 64.000000 2073.000000 5.000000 0.0 ... 43.000000 27.000000 18.000000 4018.000000 741.000000 804.000000 5544.000000 40.000000 409.000000 12.000000

8 rows × 23396 columns

In [ ]:
hcc_unfiltered_transposed_mean_max_value = hcc_unfiltered_transposed_summary.iloc[1].max()
print("Max mean value in unfiltered transposed data:", hcc_unfiltered_transposed_mean_max_value)
hcc_unfiltered_transposed_mean_min_value = hcc_unfiltered_transposed_summary.iloc[1].min()
print("Min mean value in unfiltered transposed data:", hcc_unfiltered_transposed_mean_min_value)

Max mean value in unfiltered transposed data: 27087.0
Min mean value in unfiltered transposed data: 0.00823045267489712
In [ ]:
sns.displot(hcc_unfiltered_transposed_summary.iloc[1], bins=500)
plt.xlim(0, 27000)
plt.ylim(0, 1500)
plt.title("Distribution of mean in unfiltered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Histograms suggests that almost all elements have mean close to 0

In [ ]:
hcc_filtered_transposed_mean_max_value = hcc_filtered_transposed_summary.iloc[1].max()
print("Max mean value in filtered transposed data:", hcc_filtered_transposed_mean_max_value)
hcc_filtered_transposed_mean_min_value = hcc_filtered_transposed_summary.iloc[1].min()
print("Min mean value in filtered transposed data:", hcc_filtered_transposed_mean_min_value)

Max mean value in filtered transposed data: 28423.713656387667
Min mean value in filtered transposed data: 0.02643171806167401
In [ ]:
sns.displot(hcc_filtered_transposed_summary.iloc[1], bins=500)
plt.xlim(0, 27000)
plt.ylim(0, 1500)
plt.title("Distribution of mean in filtered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Histograms suggests that almost all elements have mean close to 0

In [ ]:
hcc_removed_elements_transposed_mean_max_value = hcc_removed_elements_transposed_summary.iloc[1].max()
print("Max value in removed elements transposed data:", hcc_removed_elements_transposed_mean_max_value)
hcc_removed_elements_transposed_mean_min_value = hcc_removed_elements_transposed_summary.iloc[1].min()
print("Min value in removed elements transposed data:", hcc_removed_elements_transposed_mean_min_value)

Max value in removed elements transposed data: 8842.5
Min value in removed elements transposed data: 0.0
In [ ]:
sns.displot(hcc_removed_elements_transposed_summary.iloc[1], bins=500)
plt.xlim(0, 6100)
plt.ylim(0, 1500)
plt.title("Distribution of mean in removed elements")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Histograms suggests that almost all elements have mean close to 0

In [ ]:
# is cell with max mean value in filtered_tranposed and unfiltered_transposed data the same?

max_mean_column_filtered_transposed = hcc_filtered_transposed.mean().idxmin()
max_mean_column_unfiltered_transposed = hcc_unfiltered_transposed.mean().idxmin()

print("is cell with max mean value in filtered transposed and unfiltered transposed data the same?", max_mean_column_filtered_transposed==max_mean_column_unfiltered_transposed,
        "\nmax mean column in filtered data:", max_mean_column_filtered_transposed, " and the value is:", hcc_filtered_transposed.mean().max(),
        "\nmax mean column in unfiltered data:", max_mean_column_unfiltered_transposed, " and the value is:", hcc_unfiltered_transposed.mean().max())

is cell with max mean value in filtered transposed and unfiltered transposed data the same? False 
max mean column in filtered data: "EFCAB14P1"  and the value is: 28423.713656387667 
max mean column in unfiltered data: "CICP3"  and the value is: 27087.0
In [ ]:
# is cell with highest mean value in unfiltered contained in filtered?

if max_mean_column_unfiltered_transposed in hcc_filtered_transposed:
    print("yes")
else:
    print("no")

no
In [ ]:
# is cell with lowest mean value in filtered_tranposed and unfiltered_transposed data the same?

min_mean_column_filtered_transposed = hcc_filtered_transposed.mean().idxmin()
min_mean_column_unfiltered_transposed = hcc_unfiltered_transposed.mean().idxmin()

print("is cell with lowest mean value in filtered transposed and unfiltered transposed data the same?", min_mean_column_filtered_transposed==min_mean_column_unfiltered_transposed,
        "\nmin mean column in filtered data:", min_mean_column_filtered_transposed, " and the value is:", hcc_filtered_transposed.mean().min(),
        "\nmin mean column in unfiltered data:", min_mean_column_unfiltered_transposed, " and the value is:", hcc_unfiltered_transposed.mean().min())

is cell with lowest mean value in filtered transposed and unfiltered transposed data the same? False 
min mean column in filtered data: "EFCAB14P1"  and the value is: 0.02643171806167401 
min mean column in unfiltered data: "CICP3"  and the value is: 0.00823045267489712
In [ ]:
# is cell with lowest mean value in unfiltered contained in filtered?

if min_mean_column_unfiltered_transposed in hcc_filtered_transposed:
    print("yes")
else:
    print("no")

no
¶

2.2.2.2) Study of STD

In [ ]:
hcc_unfiltered_transposed_std_max_value = hcc_unfiltered_transposed_summary.iloc[2].max()
print("Max std value in unfiltered transposed data:", hcc_unfiltered_transposed_std_max_value)
hcc_unfiltered_transposed_std_min_value = hcc_unfiltered_transposed_summary.iloc[2].min()
print("Min std value in unfiltered transposed data:", hcc_unfiltered_transposed_std_min_value)

Max std value in unfiltered transposed data: 24669.076098596488
Min std value in unfiltered transposed data: 0.0905342064629544
In [ ]:
sns.displot(hcc_unfiltered_transposed_summary.iloc[2], bins=500)
plt.xlim(0, 27200)
plt.ylim(0, 1500)
plt.title("Distribution of STD in unfiltered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Histograms suggests that almost all elements have STD close to 0

In [ ]:
hcc_filtered_transposed_std_max_value = hcc_filtered_transposed_summary.iloc[2].max()
print("Max std value in filtered transposed data:", hcc_filtered_transposed_mean_max_value)
hcc_filtered_transposed_std_min_value = hcc_filtered_transposed_summary.iloc[2].min()
print("Min std value in filtered transposed data:", hcc_filtered_transposed_mean_min_value)

Max std value in filtered transposed data: 28423.713656387667
Min std value in filtered transposed data: 0.02643171806167401
In [ ]:
sns.displot(hcc_filtered_transposed_summary.iloc[2], bins=1000)
plt.xlim(0, 27000)
plt.ylim(0, 1000)
plt.title("Distribution of STD in filtered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Histograms suggests that almost all elements have STD close to 0

In [ ]:
hcc_removed_elements_transposed_std_max_value = hcc_removed_elements_transposed_summary.iloc[2].max()
print("Max value std in removed elements transposed data:", hcc_removed_elements_transposed_mean_max_value)
hcc_removed_elements_transposed_mean_min_value = hcc_removed_elements_transposed_summary.iloc[2].min()
print("Min value std in removed elements transposed data:", hcc_removed_elements_transposed_mean_min_value)

Max value std in removed elements transposed data: 8842.5
Min value std in removed elements transposed data: 0.0
In [ ]:
sns.displot(hcc_removed_elements_transposed_summary.iloc[2], bins=1000)
plt.xlim(0, 6100)
plt.ylim(0, 3000)
plt.title("Distribution of STD in removed elements")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

Histograms suggests that almost all elements have STD close to 0

In [ ]:
# is cell with max mean value in filtered_tranposed and unfiltered_transposed data the same?

max_std_column_filtered_transposed = hcc_filtered_transposed.std().idxmin()
max_std_column_unfiltered_transposed = hcc_unfiltered_transposed.std().idxmin()

print("is cell with max std value in filtered transposed and unfiltered transposed data the same?", max_std_column_filtered_transposed==max_std_column_unfiltered_transposed,
        "\nmax std column in filtered data:", max_std_column_filtered_transposed, " and the value is:", hcc_filtered_transposed.std().max(),
        "\nmax std column in unfiltered data:", max_std_column_unfiltered_transposed, " and the value is:", hcc_unfiltered_transposed.std().max())

is cell with max std value in filtered transposed and unfiltered transposed data the same? False 
max std column in filtered data: "MIR6766"  and the value is: 24582.568596498753 
max std column in unfiltered data: "SBF1P2"  and the value is: 24669.076098596484
In [ ]:
# is cell with highest mean value in unfiltered contained in filtered?

if max_std_column_unfiltered_transposed in hcc_filtered_transposed:
    print("yes")
else:
    print("no")

no
In [ ]:
# is cell with lowest std value in filtered_tranposed and unfiltered_transposed data the same?

min_std_column_filtered_transposed = hcc_filtered_transposed.std().idxmin()
min_std_column_unfiltered_transposed = hcc_unfiltered_transposed.std().idxmin()

print("is cell with lowest std value in filtered transposed and unfiltered transposed data the same?", min_std_column_filtered_transposed==min_std_column_unfiltered_transposed,
        "\nmin std column in filtered data:", min_std_column_filtered_transposed, " and the value is:", hcc_filtered_transposed.std().min(),
        "\nmin std column in unfiltered data:", min_std_column_unfiltered_transposed, " and the value is:", hcc_unfiltered_transposed.std().min())

is cell with lowest std value in filtered transposed and unfiltered transposed data the same? False 
min std column in filtered data: "MIR6766"  and the value is: 0.1607698527031286 
min std column in unfiltered data: "SBF1P2"  and the value is: 0.0905342064629544
In [ ]:
# is cell with lowest mean value in unfiltered contained in filtered?

if min_std_column_unfiltered_transposed in hcc_filtered_transposed:
    print("yes")
else:
    print("no")

no
¶

2.2.2.3) Details on distribution of mean

In [ ]:
hcc_unfiltered_transposed_summary.iloc[1].describe()

count    23396.000000
mean        86.010698
std        421.536457
min          0.008230
25%          0.279835
50%          7.187243
75%         60.075103
max      27087.000000
Name: mean, dtype: float64

All statistics confirm what seen in histogram: majority of elements have low mean (close to 0) and few have an high one

In [ ]:
hcc_filtered_transposed_summary.iloc[1].describe()

count    19503.000000
mean       107.439569
std        480.983441
min          0.026432
25%          1.303965
50%         17.405286
75%         83.696035
max      28423.713656
Name: mean, dtype: float64

All statistics confirm what seen in histogram: majority of elements have low mean (close to 0) and few have an high one

In [ ]:
hcc_removed_elements_transposed_summary.iloc[1].describe()

count    23396.000000
mean        35.366508
std        151.701045
min          0.000000
25%          0.062500
50%          3.562500
75%         29.000000
max       8842.500000
Name: mean, dtype: float64

All statistics confirm what seen in histogram: majority of elements have low mean (close to 0) and few have an high one

¶

2.2.2.4) Details of distribution of STD

In [ ]:
hcc_unfiltered_transposed_summary.iloc[2].describe()

count    23396.000000
mean        87.586074
std        389.473821
min          0.090534
25%          1.590075
50%         18.227290
75%         77.864077
max      24669.076099
Name: std, dtype: float64

All statistics confirm what seen in histogram: majority of elements have low mean (close to 0) and few have an high one

In [ ]:
hcc_filtered_transposed_summary.iloc[2].describe()

count    19503.000000
mean       102.497791
std        417.430333
min          0.160770
25%          4.977498
50%         31.510106
75%         94.310478
max      24582.568596
Name: std, dtype: float64

All statistics confirm what seen in histogram: majority of elements have low mean (close to 0) and few have an high one

In [ ]:
hcc_removed_elements_transposed_summary.iloc[2].describe()

count    23396.000000
mean        85.593360
std        389.579492
min          0.000000
25%          0.250000
50%         10.955876
75%         72.964289
max      21782.276646
Name: std, dtype: float64

All statistics confirm what seen in histogram: majority of elements have low mean (close to 0) and few have an high one

¶

2.2.3) TRIAL AND ERROR OF PSEUDO FILTERING ON CELLS

We used same approach applied before (see 2.1.3)

In [ ]:
# similarity between filtered and unfiltred data

filtered_set = set(hcc_filtered.columns)
unfiltered_set = set(hcc_unfiltered.columns)

intersection = filtered_set.intersection(unfiltered_set)
union = filtered_set.union(unfiltered_set)

similarity = len(intersection) / len(union)

removed = unfiltered_set - filtered_set

print(f"Similarity Percentage: {similarity}%", "\nnumber of removed:", len(removed))

Similarity Percentage: 0.934156378600823% 
number of removed: 16
In [ ]:
# trial 1: filter removing cells with mean below 14 and std below 60

# Filter columns based on mean and standard deviation
filtered_columns = hcc_unfiltered.columns[(hcc_unfiltered.mean() >= 14) & (hcc_unfiltered.std() >= 81)]

# Create pseudo_filtered dataset
pseudo_filtered = hcc_unfiltered[filtered_columns]


# mean over 14: 98.26839826839827
# mean over 15: Similarity Percentage: 97.83549783549783%
In [ ]:
pseudo_filtered_summary=pseudo_filtered.describe()
pseudo_filtered.describe()
"output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A9_Normoxia_S20_Aligned.sortedByCoord.out.bam" ... "output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam"
count 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 ... 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000
mean 99.565695 207.678278 150.689007 35.700504 47.088434 152.799453 135.869422 38.363908 45.512139 80.726064 ... 76.361771 105.566593 54.026116 29.763806 28.905411 104.740725 35.181569 108.197940 37.279962 76.303855
std 529.532443 981.107905 976.936548 205.885369 545.367706 864.974182 870.729740 265.062493 366.704721 446.951678 ... 346.659348 536.881574 344.068304 186.721266 135.474736 444.773045 170.872090 589.082268 181.398951 369.090274
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 ... 1.000000 0.000000 0.000000 0.000000 0.000000 2.000000 0.000000 0.000000 0.000000 1.000000
75% 51.000000 125.000000 40.000000 22.000000 17.000000 81.000000 76.000000 22.000000 18.000000 57.000000 ... 56.000000 67.000000 29.000000 18.000000 19.000000 76.000000 24.000000 68.000000 22.000000 44.000000
max 35477.000000 69068.000000 70206.000000 17326.000000 47442.000000 43081.000000 62813.000000 30240.000000 35450.000000 42310.000000 ... 19629.000000 30987.000000 21894.000000 13457.000000 11488.000000 33462.000000 15403.000000 34478.000000 10921.000000 28532.000000

8 rows × 231 columns

In [ ]:
pseudo_filtered_mean_max_value = pseudo_filtered_summary.iloc[1].max()
print("max mean value in pseudo filtered:", pseudo_filtered_mean_max_value)
hcc_filtered_mean_min_value = pseudo_filtered_summary.iloc[1].min()
print("min mean value in pseudo filtered:", hcc_filtered_mean_min_value)

max mean value in pseudo filtered: 246.11608822020858
min mean value in pseudo filtered: 14.624807659428962
In [ ]:
sns.displot(pseudo_filtered_summary.iloc[1], bins=100)
plt.xlim(0, 105)
plt.ylim(0, 30)
plt.title("Distribution of means in pseudo filtered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
pseudo_filtered_std_max_value = pseudo_filtered_summary.iloc[2].max()
print("max STD value in pseudo filtered:", pseudo_filtered_std_max_value)
hcc_filtered_std_min_value = pseudo_filtered_summary.iloc[2].min()
print("min STD value in pseudo filtered:", hcc_filtered_std_min_value)

max STD value in pseudo filtered: 1794.9847811738643
min STD value in pseudo filtered: 81.56511437149355
In [ ]:
sns.displot(pseudo_filtered_summary.iloc[2], bins=100)
plt.xlim(0, 1700) 
plt.ylim(0, 20)
plt.title("Distribution of STD in pseudo filtered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
# similarity between filtered and pseudo_filtered data

# Convert the dataframes to sets
filtered_set = set(hcc_filtered.columns)
pseudo_set = set(pseudo_filtered.columns)
unfiltered_set = set(hcc_unfiltered.columns)

# Calculate the intersection and union of the sets
intersection = filtered_set.intersection(pseudo_set)
union = filtered_set.union(pseudo_set)

# Calculate the Jaccard similarity coefficient
similarity = len(intersection) / len(union)

# Convert the similarity to percentage
similarity_percentage = similarity * 100

rem=unfiltered_set - pseudo_set
diff = pseudo_set - filtered_set #elements in pseudo_filtered that are not in filtered
diff1 = filtered_set - pseudo_set #elements in filtered that are not in pseudo_filtered

# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage}%", "\nnumber of removed", len(rem), 
      "\nnumber of elements in pseudo_filtered that are not in filtered:", len(diff), 
      "\nnumber of elemtns in filtered that are not in pseudo_filtered:", len(diff1))

Similarity Percentage: 98.26839826839827% 
number of removed 12 
number of elements in pseudo_filtered that are not in filtered: 4 
number of elemtns in filtered that are not in pseudo_filtered: 0

Check whether mean=14 and STD=60 are good lower bounds

In [ ]:
#check 1: reduce mean to 8 and std to 60

# Filter columns based on mean and standard deviation
filtered_columns_check1 = hcc_unfiltered.columns[(hcc_unfiltered.mean() >= 8) & (hcc_unfiltered.std() >= 60)]

# Create pseudo_filtered dataset
pseudo_filtered_check1 = hcc_unfiltered[filtered_columns_check1]

pseudo_filtered_check1_summary=pseudo_filtered_check1.describe()
pseudo_filtered_check1.describe()

# similarity between filtered and _check data

# Convert the dataframes to sets
filtered_set1 = set(hcc_filtered.columns)
pseudo_set_check1 = set(pseudo_filtered_check1.columns)
unfiltered_set1 = set(hcc_unfiltered.columns)

# Calculate the intersection and union of the sets
intersection_check1 = filtered_set1.intersection(pseudo_set_check1)
union_check1 = filtered_set1.union(pseudo_set_check1)

# Calculate the Jaccard similarity coefficient
similarity_check1 = len(intersection_check1) / len(union_check1)

# Convert the similarity to percentage
similarity_percentage_check1 = similarity_check1 * 100

rem_check1=unfiltered_set - pseudo_set_check1
diff__check1 = pseudo_set_check1 - filtered_set #elements in _check that are not in filtered
diff1__check1 = filtered_set - pseudo_set_check1 #elements in filtered that are not in _check

# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage_check1}%", "\nnumber of removed", len(rem_check1), "\nnumber of elements in _check that are not in filtered:", len(diff__check1), "\nnumber of elemtns in filtered that are not in _check:", len(diff1__check1))

# I removed 66 elements. 7 of these are mistakes. still need to remove 11 elements

Similarity Percentage: 97.84482758620689% 
number of removed 11 
number of elements in _check that are not in filtered: 5 
number of elemtns in filtered that are not in _check: 0
In [ ]:
#check 3: increase mean to 16 & std to 100

# Filter columns based on mean and standard deviation
filtered_columns_check3 = hcc_unfiltered.columns[(hcc_unfiltered.mean() >= 16) & (hcc_unfiltered.std() >= 100)]

# Create pseudo_filtered dataset
pseudo_filtered_check3 = hcc_unfiltered[filtered_columns_check3]

pseudo_filtered_check3_summary=pseudo_filtered_check3.describe()

# similarity between filtered and _check data

# Convert the dataframes to sets
filtered_set = set(hcc_filtered.columns)
pseudo_set_check3 = set(pseudo_filtered_check3.columns)
unfiltered_set = set(hcc_unfiltered.columns)

# Calculate the intersection and union of the sets
intersection_check3 = filtered_set.intersection(pseudo_set_check3)
union_check3 = filtered_set.union(pseudo_set_check3)

# Calculate the Jaccard similarity coefficient
similarity_check3 = len(intersection_check3) / len(union_check3)

# Convert the similarity to percentage
similarity_percentage_check3 = similarity_check3 * 100

rem_check3=unfiltered_set - pseudo_set_check3
diff__check3 = pseudo_set - filtered_set #elements in _check that are not in filtered
diff1__check3 = filtered_set - pseudo_set_check3 #elements in filtered that are not in _check

# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage_check3}%", "\nnumber of removed", len(rem_check3), 
      "\nnumber of elements in _check that are not in filtered:", len(diff__check3), 
      "\nnumber of elemtns in filtered that are not in _check:", len(diff1__check3))

# I removed 66 elements. 7 of these are mistakes. still need to remove 11 elements

Similarity Percentage: 96.96969696969697% 
number of removed 15 
number of elements in _check that are not in filtered: 4 
number of elemtns in filtered that are not in _check: 3

THUS MEAN=14 and STD=82 are best LOWER BOUNDS

In [ ]:
# trial 2: remove elements with mean over 210

# since element with max std in unfiltered is also in filtered, I will not put an upper bound on std

# Filter columns based on mean and standard deviation
filtered_columns2 = pseudo_filtered.columns[(pseudo_filtered.mean() < 210)]

# Create pseudo_filtered dataset
pseudo_filtered2= hcc_unfiltered[filtered_columns2]
In [ ]:
pseudo_filtered2_summary=pseudo_filtered2.describe()
pseudo_filtered2.describe()
"output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A9_Normoxia_S20_Aligned.sortedByCoord.out.bam" ... "output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam"
count 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 ... 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000 23396.000000
mean 99.565695 207.678278 150.689007 35.700504 47.088434 152.799453 135.869422 38.363908 45.512139 80.726064 ... 76.361771 105.566593 54.026116 29.763806 28.905411 104.740725 35.181569 108.197940 37.279962 76.303855
std 529.532443 981.107905 976.936548 205.885369 545.367706 864.974182 870.729740 265.062493 366.704721 446.951678 ... 346.659348 536.881574 344.068304 186.721266 135.474736 444.773045 170.872090 589.082268 181.398951 369.090274
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 ... 1.000000 0.000000 0.000000 0.000000 0.000000 2.000000 0.000000 0.000000 0.000000 1.000000
75% 51.000000 125.000000 40.000000 22.000000 17.000000 81.000000 76.000000 22.000000 18.000000 57.000000 ... 56.000000 67.000000 29.000000 18.000000 19.000000 76.000000 24.000000 68.000000 22.000000 44.000000
max 35477.000000 69068.000000 70206.000000 17326.000000 47442.000000 43081.000000 62813.000000 30240.000000 35450.000000 42310.000000 ... 19629.000000 30987.000000 21894.000000 13457.000000 11488.000000 33462.000000 15403.000000 34478.000000 10921.000000 28532.000000

8 rows × 230 columns

In [ ]:
pseudo_filtered2_mean_max_value = pseudo_filtered2_summary.iloc[1].max()
print("max mean value in pseudo filtered2:", pseudo_filtered2_mean_max_value)
hcc_filtered2_mean_min_value = pseudo_filtered2_summary.iloc[1].min()
print("min mean value in pseudo filtered2:", hcc_filtered2_mean_min_value)

max mean value in pseudo filtered2: 207.67827833817748
min mean value in pseudo filtered2: 14.624807659428962
In [ ]:
sns.displot(pseudo_filtered2_summary.iloc[1], bins=100)
plt.xlim(0, 105) 
plt.ylim(0, 30)
plt.title("Distribution of means in pseudo filtered2")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
pseudo_filtered2_std_max_value = pseudo_filtered2_summary.iloc[2].max()
print("max std value in pseudo filtered2:", pseudo_filtered2_std_max_value)
hcc_filtered2_std_min_value = pseudo_filtered2_summary.iloc[2].min()
print("min std value in pseudo filtered2:", hcc_filtered2_std_min_value)

max std value in pseudo filtered2: 1794.9847811738643
min std value in pseudo filtered2: 81.56511437149355
In [ ]:
sns.displot(pseudo_filtered2_summary.iloc[2], bins=100)
plt.xlim(0, 1700) 
plt.ylim(0, 20)
plt.title("Distribution of STD in pseudo filtered2")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
# similarity between filtered and pseudo_filtered2 data

# Convert the dataframes to sets
filtered_set = set(hcc_filtered.columns)
pseudo_set2 = set(pseudo_filtered2.columns)
unfiltered_set = set(hcc_unfiltered.columns)

# Calculate the intersection and union of the sets
intersection2 = filtered_set.intersection(pseudo_set2)
union2 = filtered_set.union(pseudo_set2)

# Calculate the Jaccard similarity coefficient
similarity2 = len(intersection2) / len(union2)

# Convert the similarity to percentage
similarity_percentage2 = similarity2 * 100

rem2=unfiltered_set - pseudo_set2
diff_2 = pseudo_set2 - filtered_set #elements in pseudo_filtered3 that are not in filtered
diff1_2 = filtered_set - pseudo_set2 # in filtered that are not in pseudo_filtered3

# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage2}%", "\nnumber of removed", len(rem2),
      "\nnumber of elements in pseudo_filtered2 that are not in filtered:", len(diff_2), 
      "\nnumber of elemtns in filtered that are not in pseudo_filtered2:", len(diff1_2))

# I removed 66 elements. 7 of these are mistakes. still need to remove 11 elements

98.26839826839827

Similarity Percentage: 98.69565217391305% 
number of removed 13 
number of elements in pseudo_filtered2 that are not in filtered: 3 
number of elemtns in filtered that are not in pseudo_filtered2: 0

98.26839826839827

Check if mean=210 is a good upper bound

In [ ]:
# check 1: reduce mean to 207
# NOTE: with this upper bound does I remove 1 elemnent and mean does not change. Thus do checks using higher upper bounds may not help 

# Filter columns based on mean and standard deviation
filtered_columns4 = pseudo_filtered2.columns[(pseudo_filtered2.mean() < 207)]

# Create pseudo_filtered dataset
pseudo_filtered4 = hcc_unfiltered[filtered_columns4]

pseudo_filtered4_summary=pseudo_filtered4.describe()

# similarity between filtered and pseudo_filtered4 data

# Convert the dataframes to sets
filtered_set = set(hcc_filtered.columns)
pseudo_set4 = set(pseudo_filtered4.columns)
unfiltered_set = set(hcc_unfiltered.columns)

# Calculate the intersection and union of the sets
intersection4 = filtered_set.intersection(pseudo_set4)
union4 = filtered_set.union(pseudo_set4)

# Calculate the Jaccard similarity coefficient
similarity4 = len(intersection4) / len(union4)

# Convert the similarity to percentage
similarity_percentage4 = similarity4 * 100

rem4=unfiltered_set - pseudo_set4
diff_4 = pseudo_set4 - filtered_set #elements in pseudo_filtered3 that are not in filtered
diff1_4 = filtered_set - pseudo_set4 #elements in filtered that are not in pseudo_filtered3

# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage4}%", "\nnumber of removed", len(rem4), 
      "\nnumber of elements in pseudo_filtered2 that are not in filtered:", len(diff_4), 
      "\nnumber of elemtns in filtered that are not in pseudo_filtered2:", len(diff1_4))

Similarity Percentage: 97.82608695652173% 
number of removed 15 
number of elements in pseudo_filtered2 that are not in filtered: 3 
number of elemtns in filtered that are not in pseudo_filtered2: 2
In [ ]:
# check 3, put a bound on std at 1700 (remove just 1 element)

# Filter columns based on mean and standard deviation
filtered_columns6 = pseudo_filtered2.columns[(pseudo_filtered2.std() < 1700)]

# Create pseudo_filtered dataset
pseudo_filtered6 = hcc_unfiltered[filtered_columns6]

pseudo_filtered6_summary=pseudo_filtered6.describe()

# similarity between filtered and pseudo_filtered6 data

# Convert the dataframes to sets
filtered_set = set(hcc_filtered.columns)
pseudo_set6 = set(pseudo_filtered6.columns)
unfiltered_set = set(hcc_unfiltered.columns)

# Calculate the intersection and union of the sets
intersection6 = filtered_set.intersection(pseudo_set6)
union6 = filtered_set.union(pseudo_set6)

# Calculate the Jaccard similarity coefficient
similarity6 = len(intersection6) / len(union6)

# Convert the similarity to percentage
similarity_percentage6 = similarity6 * 100

rem6=unfiltered_set - pseudo_set6
diff_6 = pseudo_set6 - filtered_set #elements in pseudo_filtered3 that are not in filtered
diff1_6 = filtered_set - pseudo_set6 #elements in filtered that are not in pseudo_filtered3

# Print the similarity percentage
print(f"Similarity Percentage: {similarity_percentage6}%", "\nnumber of removed", len(rem6), 
      "\nnumber of elements in pseudo_filtered2 that are not in filtered:", len(diff_6), 
      "\nnumber of elemtns in filtered that are not in pseudo_filtered2:", len(diff1_6))

Similarity Percentage: 98.26086956521739% 
number of removed 14 
number of elements in pseudo_filtered2 that are not in filtered: 3 
number of elemtns in filtered that are not in pseudo_filtered2: 1

MEAN below 210 and no upper bound on STD is best filtering

FILTERING MANTAINING CELLS WITH MEAN OVER GENES BETWEEN 14 AND 210 AND STD OVER 82 WE REACH A SIMILIARITY PERCENTAGE WITH FILTERED DATAFRAME OF 98.26086956521739% :

  • TOTAL REMOVED 16

  • NUMBER OF ERRORS WHILE PSEUDO FILTERING: 3

  • NUMBER OF MISSED ELEMENTS WHILE PSEUDO FILTERING: 3

¶

2.2.4) TRIAL AND ERROR OF PSEUDO FILTERING ON GENES

We use same approach done before (see 2.1.4)

In [ ]:
# similarity between filtered_transposed and unfiltred_transposed data

filtered_transposed_set=set(hcc_filtered_transposed.columns)
unfiltered_transposed_set=set(hcc_unfiltered_transposed.columns)

intersection=filtered_transposed_set.intersection(unfiltered_transposed_set)
union=filtered_transposed_set.union(unfiltered_transposed_set)

similarity_transposed=len(intersection)/len(union)
removed_transposed=unfiltered_transposed_set - filtered_transposed_set

print(f"Similarity Percentage: {similarity_transposed}%", "\nnumber of removed:", len(removed_transposed))

Similarity Percentage: 0.8336040348777569% 
number of removed: 3893
In [ ]:
#trial 1: filter genes with mean below 0.8 and no filter on std
filtered_transposed_columns = hcc_unfiltered_transposed.columns[(hcc_unfiltered_transposed.mean() >= 0.05)]
pseudo_transposed_filtered = hcc_unfiltered_transposed[filtered_transposed_columns]
pseudo_transposed_filtered_summary=pseudo_transposed_filtered.describe()
pseudo_transposed_filtered.describe()
"CICP27" "DDX11L17" "WASH9P" "OR4F29" "MTND1P23" "MTND2P28" "MTCO1P12" "MTCO2P12" "MTATP8P1" "MTATP6P1" ... "MT-TH" "MT-TS2" "MT-TL2" "MT-ND5" "MT-ND6" "MT-TE" "MT-CYB" "MT-TT" "MT-TP" "MAFIP"
count 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 ... 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000 243.000000
mean 0.119342 0.469136 0.255144 0.127572 117.930041 28.427984 904.308642 1.403292 0.378601 507.098765 ... 10.358025 5.930041 13.493827 2290.213992 386.901235 18.246914 2163.588477 20.613169 46.444444 3.897119
std 0.594531 1.455282 0.818639 0.440910 103.038022 26.062662 654.520308 1.735003 0.747361 394.423565 ... 10.910199 7.040559 11.803967 1726.423259 309.276105 54.076514 1730.393947 22.224590 47.684223 4.736193
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 46.500000 8.000000 390.500000 0.000000 0.000000 211.000000 ... 2.000000 1.000000 4.000000 918.000000 138.500000 4.000000 947.500000 5.000000 14.000000 0.000000
50% 0.000000 0.000000 0.000000 0.000000 94.000000 22.000000 790.000000 1.000000 0.000000 428.000000 ... 7.000000 4.000000 10.000000 1848.000000 320.000000 11.000000 1774.000000 14.000000 38.000000 2.000000
75% 0.000000 0.000000 0.000000 0.000000 157.000000 42.000000 1208.000000 2.000000 1.000000 684.000000 ... 14.000000 7.500000 20.000000 3172.000000 528.000000 20.000000 2927.000000 30.500000 64.500000 6.000000
max 5.000000 12.000000 6.000000 4.000000 694.000000 120.000000 3569.000000 12.000000 4.000000 2358.000000 ... 52.000000 43.000000 57.000000 8972.000000 1439.000000 804.000000 11383.000000 154.000000 409.000000 24.000000

8 rows × 20771 columns

In [ ]:
pseudo_filtered_transposed_mean_max_value = pseudo_transposed_filtered_summary.iloc[1].max()
print("max mean value in pseudo filtered:", pseudo_filtered_transposed_mean_max_value)
hcc_filtered_transposed_mean_min_value = pseudo_transposed_filtered_summary.iloc[1].min()
print("min mean value in pseudo filtered:", hcc_filtered_transposed_mean_min_value)

max mean value in pseudo filtered: 27087.0
min mean value in pseudo filtered: 0.053497942386831275
In [ ]:
sns.displot(pseudo_transposed_filtered_summary.iloc[1], bins=500)
plt.xlim(0, 25000) 
plt.ylim(0, 1500)
plt.title("Distribution of means in pseudo pseudo_transposed_filtered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image

All statistics confirm what seen in histogram: majority of elements have low mean (close to 0) and few have an high one

In [ ]:
pseudo_filtered_transposed_std_max_value = pseudo_transposed_filtered_summary.iloc[2].max()
print("max std value in pseudo filtered:", pseudo_filtered_transposed_std_max_value)
hcc_filtered_transposed_mean_min_value = pseudo_transposed_filtered_summary.iloc[2].min()
print("min std value in pseudo filtered:", hcc_filtered_transposed_std_min_value)

max std value in pseudo filtered: 24669.076098596488
min std value in pseudo filtered: 0.1607698527031286
In [ ]:
sns.displot(pseudo_transposed_filtered_summary.iloc[2], bins=500)
plt.xlim(0, 27200) 
plt.ylim(0, 1500)
plt.title("Distribution of STD in pseudo pseudo_transposed_filtered")
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
# similarity between filtered_transposed and pseudo_transposed_filtered data

filtered_transposed_set=set(hcc_filtered_transposed.columns)
pseudo_transposed_filtered_set=set(pseudo_transposed_filtered.columns)
unfiltered_transposed_set=set(hcc_unfiltered_transposed.columns)

intersection=filtered_transposed_set.intersection(pseudo_transposed_filtered_set)
union=filtered_transposed_set.union(pseudo_transposed_filtered)

similarity_transposed=len(intersection)/len(union)
similarity_percentage_transposed=similarity_transposed*100

removed=unfiltered_transposed_set - pseudo_transposed_filtered_set
diff_transposed=pseudo_transposed_filtered_set-filtered_transposed_set
diff1_transposed=filtered_transposed_set-pseudo_transposed_filtered_set

print(f"Similarity Percentage: {similarity_percentage_transposed}%", "\nnumber of removed:", len(removed), 
      "\nnumber of elements in pseudo_filtered_transposed that are not in filtered_transposed:", len(diff_transposed), 
      "\nnumber of elemtns in filtered_transposed that are not in pseudo_filtered_transposed:", len(diff1_transposed))

Similarity Percentage: 89.51578749235331% 
number of removed: 2625 
number of elements in pseudo_filtered_transposed that are not in filtered_transposed: 1748 
number of elemtns in filtered_transposed that are not in pseudo_filtered_transposed: 480
In [ ]:
# check 1 higher thresholds (mean=0.08)

filtered_transposed_columns_check = hcc_unfiltered_transposed.columns[(hcc_unfiltered_transposed.mean() >= 0.08)]
pseudo_transposed_filtered_check = hcc_unfiltered_transposed[filtered_transposed_columns_check]
pseudo_transposed_filtered_check_summary=pseudo_transposed_filtered_check.describe()

# similarity between filtered_transposed and pseudo_transposed_filtered data

filtered_transposed_set=set(hcc_filtered_transposed.columns)
pseudo_transposed_filtered_check_set=set(pseudo_transposed_filtered_check.columns)
unfiltered_transposed_set=set(hcc_unfiltered_transposed.columns)

intersection_check=filtered_transposed_set.intersection(pseudo_transposed_filtered_check_set)
union_check=filtered_transposed_set.union(pseudo_transposed_filtered_check)

similarity_transposed_check=len(intersection_check)/len(union_check)
similarity_percentage_transposed_check=similarity_transposed_check*100

removed_check=unfiltered_transposed_set - pseudo_transposed_filtered_check_set
diff_transposed_check=pseudo_transposed_filtered_check_set-filtered_transposed_set
diff1_transposed_check=filtered_transposed_set-pseudo_transposed_filtered_check_set

print(f"Similarity Percentage: {similarity_percentage_transposed_check}%", "\nnumber of removed:", len(removed_check), 
      "\nnumber of elements in pseudo_filtered_transposed that are not in filtered_transposed:", len(diff_transposed_check), 
      "\nnumber of elemtns in filtered_transposed that are not in pseudo_filtered_transposed:", len(diff1_transposed_check))

Similarity Percentage: 89.06991231012506% 
number of removed: 3442 
number of elements in pseudo_filtered_transposed that are not in filtered_transposed: 1366 
number of elemtns in filtered_transposed that are not in pseudo_filtered_transposed: 915
In [ ]:
# check 2 lower thresholds (mean=0.05)

filtered_transposed_columns_check2 = hcc_unfiltered_transposed.columns[(hcc_unfiltered_transposed.mean() >= 0.03)]
pseudo_transposed_filtered_check2 = hcc_unfiltered_transposed[filtered_transposed_columns_check2]
pseudo_transposed_filtered_check2_summary2=pseudo_transposed_filtered_check2.describe()

# similarity between filtered_transposed and pseudo_transposed_filtered data

filtered_transposed_set=set(hcc_filtered_transposed.columns)
pseudo_transposed_filtered_check2_set=set(pseudo_transposed_filtered_check2.columns)
unfiltered_transposed_set=set(hcc_unfiltered_transposed.columns)

intersection_check2=filtered_transposed_set.intersection(pseudo_transposed_filtered_check2_set)
union_check2=filtered_transposed_set.union(pseudo_transposed_filtered_check2)

similarity_transposed_check2=len(intersection_check2)/len(union_check2)
similarity_percentage_transposed_check2=similarity_transposed_check2*100

removed_check2=unfiltered_transposed_set - pseudo_transposed_filtered_check2_set
diff_transposed_check2=pseudo_transposed_filtered_check2_set-filtered_transposed_set
diff1_transposed_check2=filtered_transposed_set-pseudo_transposed_filtered_check2_set

print(f"Similarity Percentage: {similarity_percentage_transposed_check2}%", "\nnumber of removed:", len(removed_check2), 
      "\nnumber of elements in pseudo_filtered_transposed that are not in filtered_transposed:", len(diff_transposed_check2), 
      "\nnumber of elemtns in filtered_transposed that are not in pseudo_filtered_transposed:", len(diff1_transposed_check2))

Similarity Percentage: 89.11583402336922% 
number of removed: 1789 
number of elements in pseudo_filtered_transposed that are not in filtered_transposed: 2235 
number of elemtns in filtered_transposed that are not in pseudo_filtered_transposed: 131
In [ ]:
# check 3 introduce a std lower bound= 0.5

filtered_transposed_columns_check3 = hcc_unfiltered_transposed.columns[(hcc_unfiltered_transposed.std() >= 0.5)]
pseudo_transposed_filtered_check3 = hcc_unfiltered_transposed[filtered_transposed_columns_check3]
pseudo_transposed_filtered_check3_summary3=pseudo_transposed_filtered_check3.describe()

# similarity between filtered_transposed and pseudo_transposed_filtered data

filtered_transposed_set=set(hcc_filtered_transposed.columns)
pseudo_transposed_filtered_check3_set=set(pseudo_transposed_filtered_check3.columns)
unfiltered_transposed_set=set(hcc_unfiltered_transposed.columns)

intersection_check3=filtered_transposed_set.intersection(pseudo_transposed_filtered_check3_set)
union_check3=filtered_transposed_set.union(pseudo_transposed_filtered_check3)

similarity_transposed_check3=len(intersection_check3)/len(union_check3)
similarity_percentage_transposed_check3=similarity_transposed_check3*100

removed_check3=unfiltered_transposed_set - pseudo_transposed_filtered_check3_set
diff_transposed_check3=pseudo_transposed_filtered_check3_set-filtered_transposed_set
diff1_transposed_check3=filtered_transposed_set-pseudo_transposed_filtered_check3_set

print(f"Similarity Percentage: {similarity_percentage_transposed_check3}%", "\nnumber of removed:", len(removed_check3), 
      "\nnumber of elements in pseudo_filtered_transposed that are not in filtered_transposed:", len(diff_transposed_check3), 
      "\nnumber of elemtns in filtered_transposed that are not in pseudo_filtered_transposed:", len(diff1_transposed_check3))

Similarity Percentage: 84.94150678521292% 
number of removed: 3377 
number of elements in pseudo_filtered_transposed that are not in filtered_transposed: 1867 
number of elemtns in filtered_transposed that are not in pseudo_filtered_transposed: 1351

mean=0.05, std=0 is best lower bound

best option is to put no upper bound on mean and std since smiliarity will decrease. Infact:

In [ ]:
# put upper bound mean=26900, std=27100 (just 1 element is removed)

filtered_transposed_column_check2 = pseudo_transposed_filtered.columns[(pseudo_transposed_filtered.mean() <= 26900) & (pseudo_transposed_filtered.std() <= 27100)]
pseudo_transposed_filtered_check2 = pseudo_transposed_filtered[filtered_transposed_column_check2]
pseudo_transposed_filtered_check2_summary=pseudo_transposed_filtered_check2.describe()


#similarity between filtered_transposed and pseudo_transposed_filtered data

filtered_transposed_set=set(hcc_filtered_transposed.columns)
pseudo_transposed_filtered_set_check2=set(pseudo_transposed_filtered_check2.columns)
unfiltered_transposed_set=set(hcc_unfiltered_transposed.columns)

intersection_check2=filtered_transposed_set.intersection(pseudo_transposed_filtered_set_check2)
union_check2=filtered_transposed_set.union(pseudo_transposed_filtered_check2)

similarity_transposed_check2=len(intersection_check2)/len(union_check2)
similarity_percentage_transposed_check2=similarity_transposed_check2*100

removed_check2=unfiltered_transposed_set - pseudo_transposed_filtered_set_check2
diff_transposed_check2=pseudo_transposed_filtered_set_check2-filtered_transposed_set
diff1_transposed_check2=filtered_transposed_set-pseudo_transposed_filtered_set_check2

print(f"Similarity Percentage: {similarity_percentage_transposed_check2}%", "\nnumber of removed:", len(removed_check2), 
      "\nnumber of elements in pseudo_filtered_transposed that are not in filtered_transposed:", len(diff_transposed_check2), 
      "\nnumber of elemtns in filtered_transposed that are not in pseudo_filtered_transposed:", len(diff1_transposed_check2))

Similarity Percentage: 89.51108183144324% 
number of removed: 2626 
number of elements in pseudo_filtered_transposed that are not in filtered_transposed: 1748 
number of elemtns in filtered_transposed that are not in pseudo_filtered_transposed: 481

ON GENES ONLY FILTER WE CAN DO IS MEAN ABOVE 0.05 to reach Similarity Percentage: 89.51578749235331%

number of removed: 3442

number of elements in pseudo_filtered_transposed that are not in filtered_transposed: 1366

number of elemtns in filtered_transposed that are not in pseudo_filtered_transposed: 915

¶

2.2.4bis) Merging the two approaches

intersection based on cell (pseudo_filtered4 intersects pseudo_transposed_filtered transposed)

In [ ]:
intersection_cell = pseudo_filtered.columns.intersection(pseudo_transposed_filtered.T.columns)


intersection_cell_set=set(intersection_cell)
filtered_set=set(hcc_filtered.columns)
unfiltered_set=set(hcc_unfiltered.columns)

int=intersection_cell_set.intersection(filtered_set)
un=intersection_cell_set.union(filtered_set)

similarity_cell = len(int)/len(un)
similarity_cell_percentage = similarity_cell*100

removed_cell = unfiltered_set - intersection_cell_set
diff_cell = filtered_set - intersection_cell_set
diff1_cell = intersection_cell_set - filtered_set

print(f"Similarity Percentage: {similarity_cell_percentage}%", "\nnumber of removed", len(removed_cell),  
      "\nnumber of elements in cell intersection that are not in filtered:", len(diff_cell), 
        "\nnumber of elemtns in filtered that are not in cell intersection:", len(diff1_cell))

Similarity Percentage: 98.26839826839827% 
number of removed 12 
number of elements in cell intersection that are not in filtered: 0 
number of elemtns in filtered that are not in cell intersection: 4

intersection based on genes (pseudo_filtered4 transposef intersects pseudo_transposed_filtered)

In [ ]:
genes_pseudo4=pseudo_filtered2.T
intersection_genes= genes_pseudo4.columns.intersection(pseudo_transposed_filtered.columns)


intersection_genes_set=set(intersection_genes)
filtered_set_transposed=set(hcc_filtered_transposed.columns)
unfiltered_set_transposed=set(hcc_unfiltered_transposed.columns)

int_genes=intersection_genes_set.intersection(filtered_set_transposed)
un_genes=intersection_genes_set.union(filtered_set_transposed)

similarity_genes = len(int_genes)/len(un_genes)
similarity_genes_percentage = similarity_genes*100

removed_genes = unfiltered_set_transposed - intersection_genes_set
diff_genes = filtered_set_transposed - intersection_genes_set
diff1_genes = intersection_genes_set - filtered_set_transposed

print(f"Similarity Percentage: {similarity_genes_percentage}%", "\nnumber of removed", len(removed_genes),  
      "\nnumber of elements in genes intersection that are not in filtered:", len(diff_genes), 
        "\nnumber of elemtns in filtered that are not in genes intersection:", len(diff1_genes))

Similarity Percentage: 89.51578749235331% 
number of removed 2625 
number of elements in genes intersection that are not in filtered: 480 
number of elemtns in filtered that are not in genes intersection: 1748

CONCLUSION: best option is to filter firstly on cells and then intersect the result with the intersection on genes

¶

2.2.5) PCA

In [ ]:
df_filtered = pd.read_csv("SmartSeq/HCC1806_SmartS_Filtered_Data.txt",delimiter="\ ",engine='python',index_col=0)
print(df_filtered.shape)
df_filtered.head()

(19503, 227)
Out[ ]:
"output.STAR.PCRPlate1A10_Normoxia_S123_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A12_Normoxia_S26_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A2_Hypoxia_S104_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A3_Hypoxia_S4_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A4_Hypoxia_S8_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A5_Hypoxia_S108_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A6_Hypoxia_S11_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A7_Normoxia_S113_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A8_Normoxia_S119_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate1A9_Normoxia_S20_Aligned.sortedByCoord.out.bam" ... "output.STAR.PCRPlate4G12_Normoxia_S243_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" "output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam"
"CICP27" 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
"DDX11L17" 0 0 0 0 1 0 0 0 0 1 ... 0 0 0 0 0 0 0 0 0 0
"WASH9P" 0 0 0 0 0 0 0 1 0 0 ... 0 0 0 0 0 1 0 1 0 0
"OR4F29" 2 0 0 0 0 1 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
"MTND1P23" 250 424 63 27 81 305 82 46 177 114 ... 205 113 47 27 35 146 37 83 47 249

5 rows × 227 columns

In [ ]:
df_original = pd.read_csv("SmartSeq/HCC1806_SmartS_Unfiltered_Data.txt",delimiter="\ ",engine='python',index_col=0)
In [ ]:
from sklearn.decomposition import PCA
# PCA to reduce dimensionality
pca = PCA(n_components=2)  # Reducing to 50 principal components
data_filtered_reduced = pca.fit_transform(df_filtered.T)
data_original_reduced = pca.fit_transform(df_original.T)
In [ ]:
def extract_condition(name):
    match = re.search(r'(Norm|Hypo)', name)
    if match:
        return match.group(1)
    return 'Unknown'
In [ ]:
conditions = df_filtered.T.index.to_series().apply(extract_condition)
conditions_original = df_original.T.index.to_series().apply(extract_condition)
In [ ]:
plt.figure(figsize=(12, 6))

# Assign colors to conditions manually
color_map = {'Hypo': 'red', 'Norm': 'blue', 'Unknown': 'green'}
colors = conditions.map(color_map)

plt.scatter(data_filtered_reduced[:, 0], data_filtered_reduced[:, 1], c=colors)
plt.title('PCA of Cells Colored by Condition')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')

# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i], 
            label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')

plt.show()
No description has been provided for this image

In this case, despite the filtering, it is still difficult to clearly separate the data by condition. This could mean that in the HCC1806 cells the genes are less expressive of the state of the cell; however, it could also mean that the data is just not fit to be visualized in 2 dimensions.

¶

2.2.6) Clustering

¶

2.2.6.1) Single linkage

In [ ]:
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage

# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_filtered_reduced, 'single')

# Increase the figure size
plt.figure(figsize=(25, 10))  # You can adjust the size as necessary.

# Plotting a truncated dendrogram
dendrogram(
    Z,
    truncate_mode='lastp',  # show only the last p merged clusters
    p=50,  # show only the last 50 merged clusters
    leaf_rotation=90.,
    leaf_font_size=12.,
    show_contracted=True,  # to get a sense of the dataset size if truncated
)

plt.title('Hierarchical Clustering Dendrogram (Single-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
No description has been provided for this image
In [ ]:
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage

# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_original_reduced, 'single')

# Increase the figure size
plt.figure(figsize=(25, 10))  # You can adjust the size as necessary.

# Plotting a truncated dendrogram
dendrogram(
    Z,
    truncate_mode='lastp',  # show only the last p merged clusters
    p=50,  # show only the last 50 merged clusters
    leaf_rotation=90.,
    leaf_font_size=12.,
    show_contracted=True,  # to get a sense of the dataset size if truncated
)

plt.title('Hierarchical Clustering Dendrogram (Single-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
No description has been provided for this image

¶

2.2.6.2) Average Linkage

In [ ]:
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage

# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_filtered_reduced, 'average')

# Increase the figure size
plt.figure(figsize=(25, 10))  # You can adjust the size as necessary.

# Plotting a truncated dendrogram
dendrogram(
    Z,
    truncate_mode='lastp',  # show only the last p merged clusters
    p=50,  # show only the last 50 merged clusters
    leaf_rotation=90.,
    leaf_font_size=12.,
    show_contracted=True,  # to get a sense of the dataset size if truncated
)

plt.title('Hierarchical Clustering Dendrogram (Average-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
No description has been provided for this image
In [ ]:
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage

# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_original_reduced, 'average')

# Increase the figure size
plt.figure(figsize=(25, 10))  # You can adjust the size as necessary.

# Plotting a truncated dendrogram
dendrogram(
    Z,
    truncate_mode='lastp',  # show only the last p merged clusters
    p=50,  # show only the last 50 merged clusters
    leaf_rotation=90.,
    leaf_font_size=12.,
    show_contracted=True,  # to get a sense of the dataset size if truncated
)

plt.title('Hierarchical Clustering Dendrogram (-Average-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
No description has been provided for this image

¶

2.2.6.3) Complete Linkage

In [ ]:
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage

# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_filtered_reduced, 'complete')

# Increase the figure size
plt.figure(figsize=(25, 10))  # You can adjust the size as necessary.

# Plotting a truncated dendrogram
dendrogram(
    Z,
    truncate_mode='lastp',  # show only the last p merged clusters
    p=50,  # show only the last 50 merged clusters
    leaf_rotation=90.,
    leaf_font_size=12.,
    show_contracted=True,  # to get a sense of the dataset size if truncated
)

plt.title('Hierarchical Clustering Dendrogram (Complete-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
No description has been provided for this image
In [ ]:
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage

# Assuming 'data' is your pre-processed data for clustering.
Z = linkage(data_original_reduced, 'complete')

# Increase the figure size
plt.figure(figsize=(25, 10))  # You can adjust the size as necessary.

# Plotting a truncated dendrogram
dendrogram(
    Z,
    truncate_mode='lastp',  # show only the last p merged clusters
    p=50,  # show only the last 50 merged clusters
    leaf_rotation=90.,
    leaf_font_size=12.,
    show_contracted=True,  # to get a sense of the dataset size if truncated
)

plt.title('Hierarchical Clustering Dendrogram (Complete-linkage)')
plt.xlabel('Sample index or (Cluster size)')
plt.ylabel('Distance')
plt.show()
No description has been provided for this image

¶

2.2.7) Unsupervised learning applied to HCC1806

In [ ]:
# Function to extract 'Normoxia' or 'Hypoxia' from a cell name
def extract_condition(name):
    match = re.search(r'(Normoxia|Hypoxia)', name)
    if match:
        return match.group(1)
    return 'Unknown'
In [ ]:
df_HCC1806_norm_filt = pd.read_csv("SmartSeq/HCC1806_SmartS_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ",engine='python',index_col=0)
df_HCC1806_norm_filt_transpose = df_HCC1806_norm_filt.T
# We transpose the original dataset so we make the cells to be the rows since they represents the observations
# and the genes to be columns since they are the features
In [ ]:
# Apply the extract_condition function to each cell name in the DataFrame's index
conditions = df_HCC1806_norm_filt_transpose.index.to_series().apply(extract_condition)
In [ ]:
# We apply the pca to our dataset
# We basically reduce the dimensionality of our dataset to 2 dimensions
# because we want to see the data in a 2D space

pca = PCA(n_components=2)
principal_components = pca.fit_transform(df_HCC1806_norm_filt_transpose)
In [ ]:
# now based on what we have reduced we apply the k-means to try to clusters cells in two groups
# The groups should be based on the cell with Normoxia and Hypoxia
# Please notice that this k-means should use the so called k++ to initialize the centroids
# If you don't know what k++ is, please add init keywords and set it to something different
k = 2
kmeans = KMeans(n_clusters=k, init='k-means++', random_state=42)
clusters = kmeans.fit_predict(principal_components)

c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
In [ ]:
# Assign numeric labels based on conditions
condition_numeric = conditions.map({'Normoxia': 0, 'Hypoxia': 1})
In [ ]:
plt.figure(figsize=(12, 6))

# Assign colors to conditions manually
color_map = {'Hypoxia': 'red', 'Normoxia': 'blue', 'Unknown': 'green'}
colors = conditions.map(color_map)

plt.scatter(principal_components[:, 0], principal_components[:, 1], c=colors)
plt.title('PCA of Cells Colored by Condition')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')

# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i], 
            label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')

plt.show()
No description has been provided for this image
In [ ]:
# Visualization of PCA with K-means clusters, with a proper legend
plt.figure(figsize=(12, 6))

# Assign colors based on the K-means cluster result
# Here we assign 'yellow' to cluster 1 and 'blue' to cluster 0
cluster_colors = ['green' if label == 0 else 'yellow' for label in clusters]

# Scatter plot with assigned colors
scatter = plt.scatter(principal_components[:, 0], principal_components[:, 1], c=cluster_colors)

# Create a legend for the colors
plt.title('PCA of Cells Colored by K-means Clusters')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')

# Assuming that after any potential inversion, cluster 0 corresponds to Normoxia and cluster 1 to Hypoxia
legend_labels = {0: 'Cluster1', 1: 'Cluster2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
                      markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='Condition')

plt.show()
No description has been provided for this image

Unlike before with the MCF7 cells, the PCA performed on the HCC1806 cells does not give a clear cut division of the conditions of the cells. In order to have a more useful result, we will try again to see which are the genes that improve the most our ability to read the condition of the cells

¶

2.2.8) Looking for the most useful genes

In [ ]:
#i want to split the dataset in 2 parts, one with RNA with hypoxia in the name and the other with RNA with normoxia in the name
hypoxia_samples = df_HCC1806_norm_filt.T[df_HCC1806_norm_filt.T.index.str.contains('Hypo')]
normoxia_samples = df_HCC1806_norm_filt.T[df_HCC1806_norm_filt.T.index.str.contains('Norm')]
In [ ]:
hypoxia_samples
Out[ ]:
"DDIT4" "ANGPTL4" "CALML5" "KRT14" "CCNB1" "IGFBP3" "AKR1C2" "KRT6A" "NDRG1" "KRT4" ... "MST1R" "ZYG11A" "NRG1" "RBMS3" "VCPIP1" "LINC02693" "OR8B9P" "NEAT1" "ZDHHC23" "ODAD2"
"output.STAR.PCRPlate1G1_Hypoxia_S102_Aligned.sortedByCoord.out.bam" 8739 2101 55 96 1824 1938 62 0 522 413 ... 279 0 264 0 134 68 0 213 0 0
"output.STAR.PCRPlate1G2_Hypoxia_S2_Aligned.sortedByCoord.out.bam" 13098 14032 0 0 1616 247 430 907 348 0 ... 311 0 38 0 0 0 0 92 0 0
"output.STAR.PCRPlate1G3_Hypoxia_S7_Aligned.sortedByCoord.out.bam" 2880 356 0 6211 3 3430 79 1953 592 176 ... 125 0 16 0 4 1 0 1 0 0
"output.STAR.PCRPlate1G4_Hypoxia_S107_Aligned.sortedByCoord.out.bam" 7777 5661 4383 0 145 4618 246 85 206 0 ... 268 1 25 0 0 0 0 128 0 0
"output.STAR.PCRPlate1H1_Hypoxia_S103_Aligned.sortedByCoord.out.bam" 10313 850 0 2127 605 0 381 1985 138 0 ... 49 20 239 0 10 79 0 157 35 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
"output.STAR.PCRPlate4F5_Hypoxia_S229_Aligned.sortedByCoord.out.bam" 3717 2500 0 111 1447 2068 356 5 301 0 ... 153 0 280 26 111 0 0 13 0 0
"output.STAR.PCRPlate4G1_Hypoxia_S193_Aligned.sortedByCoord.out.bam" 5545 2085 1587 0 26 1085 17 806 585 0 ... 180 0 158 18 26 68 0 163 0 0
"output.STAR.PCRPlate4G2_Hypoxia_S198_Aligned.sortedByCoord.out.bam" 5757 698 0 3077 50 1408 619 2968 1775 0 ... 82 0 276 0 2 116 0 5 0 0
"output.STAR.PCRPlate4G6_Hypoxia_S232_Aligned.sortedByCoord.out.bam" 8337 2383 0 529 588 456 316 277 1461 58 ... 156 0 35 0 47 27 0 19 38 0
"output.STAR.PCRPlate4H2_Hypoxia_S199_Aligned.sortedByCoord.out.bam" 10186 3147 0 149 1012 111 716 617 744 0 ... 94 0 55 0 157 43 0 113 0 0

97 rows × 3000 columns

In [ ]:
hypoxia_summary = hypoxia_samples.describe()
normoxia_summary = normoxia_samples.describe()

normoxia_summary
Out[ ]:
"DDIT4" "ANGPTL4" "CALML5" "KRT14" "CCNB1" "IGFBP3" "AKR1C2" "KRT6A" "NDRG1" "KRT4" ... "MST1R" "ZYG11A" "NRG1" "RBMS3" "VCPIP1" "LINC02693" "OR8B9P" "NEAT1" "ZDHHC23" "ODAD2"
count 85.000000 85.000000 85.000000 85.000000 85.000000 85.000000 85.000000 85.000000 85.000000 85.000000 ... 85.000000 85.000000 85.000000 85.000000 85.000000 85.000000 85.000000 85.000000 85.000000 85.000000
mean 529.011765 46.717647 307.352941 871.094118 1211.870588 376.564706 1941.188235 1590.517647 32.400000 213.952941 ... 106.329412 5.129412 151.611765 5.435294 37.905882 67.388235 0.247059 63.223529 14.670588 2.117647
std 864.408682 170.988903 643.410494 1316.641015 1535.550582 723.431365 3121.412815 1881.465567 163.216858 811.675016 ... 118.997338 11.811806 141.017146 11.800370 48.971231 85.042300 1.479268 65.789595 34.277019 7.577240
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 3.000000 0.000000 0.000000
25% 79.000000 0.000000 0.000000 21.000000 177.000000 41.000000 291.000000 544.000000 0.000000 0.000000 ... 2.000000 0.000000 42.000000 0.000000 0.000000 2.000000 0.000000 21.000000 0.000000 0.000000
50% 247.000000 2.000000 0.000000 327.000000 489.000000 137.000000 893.000000 884.000000 1.000000 0.000000 ... 87.000000 0.000000 120.000000 0.000000 21.000000 38.000000 0.000000 42.000000 0.000000 0.000000
75% 679.000000 41.000000 163.000000 1151.000000 2207.000000 297.000000 2200.000000 1980.000000 26.000000 0.000000 ... 144.000000 1.000000 229.000000 4.000000 57.000000 109.000000 0.000000 82.000000 16.000000 0.000000
max 6278.000000 1532.000000 2867.000000 6724.000000 6914.000000 4454.000000 20195.000000 9584.000000 1507.000000 4560.000000 ... 751.000000 52.000000 763.000000 49.000000 239.000000 498.000000 11.000000 415.000000 222.000000 48.000000

8 rows × 3000 columns

In [ ]:
# I want to do the mean of the mean in the hypoxia samples and return the value
hypoxia_mean = hypoxia_samples.describe().loc['mean'].mean()
hypoxia_mean
Out[ ]:
180.94042268041235
In [ ]:
# Now let's dothe same with the standard deviation
hypoxia_std = hypoxia_samples.describe().loc['std'].mean()
hypoxia_std
Out[ ]:
148.89673483016463
In [ ]:
normoxia_samples
Out[ ]:
"DDIT4" "ANGPTL4" "CALML5" "KRT14" "CCNB1" "IGFBP3" "AKR1C2" "KRT6A" "NDRG1" "KRT4" ... "MST1R" "ZYG11A" "NRG1" "RBMS3" "VCPIP1" "LINC02693" "OR8B9P" "NEAT1" "ZDHHC23" "ODAD2"
"output.STAR.PCRPlate1G12_Normoxia_S32_Aligned.sortedByCoord.out.bam" 0 48 0 321 298 82 6250 634 0 0 ... 78 10 136 0 0 29 0 29 0 0
"output.STAR.PCRPlate1G7_Normoxia_S118_Aligned.sortedByCoord.out.bam" 208 0 1902 0 378 451 7155 2932 41 0 ... 169 0 339 0 0 1 0 56 0 0
"output.STAR.PCRPlate1G8_Normoxia_S19_Aligned.sortedByCoord.out.bam" 750 0 0 5050 66 44 7533 793 42 0 ... 0 0 0 0 0 0 0 415 0 0
"output.STAR.PCRPlate1G9_Normoxia_S121_Aligned.sortedByCoord.out.bam" 127 0 0 462 58 297 2216 26 15 0 ... 107 0 193 0 23 195 0 42 18 0
"output.STAR.PCRPlate1H9_Normoxia_S122_Aligned.sortedByCoord.out.bam" 404 32 0 1893 2796 27 1496 626 81 0 ... 0 6 201 0 239 160 0 30 23 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
"output.STAR.PCRPlate4G7_Normoxia_S204_Aligned.sortedByCoord.out.bam" 2579 65 0 156 411 147 107 1213 1 0 ... 1 0 221 0 0 1 0 4 145 0
"output.STAR.PCRPlate4H10_Normoxia_S210_Aligned.sortedByCoord.out.bam" 79 5 0 2098 554 99 798 4211 64 0 ... 11 0 264 0 10 34 0 24 0 0
"output.STAR.PCRPlate4H11_Normoxia_S214_Aligned.sortedByCoord.out.bam" 167 1 0 27 3585 134 3 596 11 0 ... 251 0 120 25 0 53 0 177 53 0
"output.STAR.PCRPlate4H7_Normoxia_S205_Aligned.sortedByCoord.out.bam" 238 41 2867 814 2207 297 1023 2879 1 0 ... 149 0 42 0 26 0 0 13 5 0
"output.STAR.PCRPlate4H9_Normoxia_S236_Aligned.sortedByCoord.out.bam" 60 0 0 7 477 10 845 1222 1 0 ... 98 7 255 0 32 25 0 13 4 0

85 rows × 3000 columns

In [ ]:
normoxia_mean = normoxia_samples.describe().loc['mean'].mean()
normoxia_mean
Out[ ]:
152.2196431372549
In [ ]:
normoxia_std = normoxia_samples.describe().loc['std'].mean()
normoxia_std
Out[ ]:
113.76597693898488
In [ ]:
#we look at how many genes have a mean of expression greater than 1 and we print their names
#for hypoxia samples    
hypoxia_samples_summary = hypoxia_samples.describe()

hypo_genes = hypoxia_samples_summary.loc['mean'][hypoxia_samples_summary.loc['mean'] > 180.94]
hypo_genes_sorted = sorted(hypo_genes.items(), key=lambda x: x[1], reverse=True)
hypo_genes_sorted # 384 genes overcome the mean threshold
Out[ ]:
[('"LDHA"', 24055.381443298967),
 ('"FTL"', 21462.113402061856),
 ('"FTH1"', 13867.432989690722),
 ('"KRT19"', 13597.38144329897),
 ('"BEST1"', 12069.711340206186),
 ('"ALDOA"', 10299.670103092783),
 ('"HSPA5"', 10243.113402061856),
 ('"CD44"', 9826.247422680412),
 ('"MT-RNR2"', 8343.61855670103),
 ('"DDIT4"', 7114.268041237114),
 ('"LAMB3"', 6433.041237113402),
 ('"HSP90B1"', 6299.298969072165),
 ('"LAMC2"', 5953.453608247422),
 ('"CD59"', 5605.412371134021),
 ('"PSMD2"', 5376.79381443299),
 ('"MT-ND4"', 5357.030927835051),
 ('"B2M"', 5326.484536082474),
 ('"PGK1"', 5289.773195876289),
 ('"MT-CO2"', 4634.030927835051),
 ('"TMSB10"', 4603.701030927835),
 ('"KRT18"', 4585.144329896907),
 ('"UBC"', 4376.5360824742265),
 ('"KRT8"', 4122.443298969072),
 ('"EZR"', 3632.783505154639),
 ('"MIF"', 3630.278350515464),
 ('"MIF-AS1"', 3620.092783505155),
 ('"ACTG1"', 3431.494845360825),
 ('"TMSB4X"', 3368.103092783505),
 ('"ANXA1"', 3352.360824742268),
 ('"CALM2"', 3079.701030927835),
 ('"CAV1"', 3077.175257731959),
 ('"HSPA8"', 3043.8865979381444),
 ('"SDHA"', 2932.381443298969),
 ('"KRT7"', 2919.659793814433),
 ('"LMNA"', 2778.4123711340208),
 ('"P4HB"', 2762.577319587629),
 ('"PRDX1"', 2740.896907216495),
 ('"SCD"', 2739.577319587629),
 ('"TUBB"', 2673.938144329897),
 ('"GPI"', 2622.773195876289),
 ('"PSAP"', 2604.938144329897),
 ('"MT-ND5"', 2507.061855670103),
 ('"SLC2A1"', 2456.9690721649486),
 ('"TUBA1B"', 2449.463917525773),
 ('"ANGPTL4"', 2261.577319587629),
 ('"CTSB"', 2224.4845360824743),
 ('"CANX"', 2199.422680412371),
 ('"TK1"', 2191.7525773195875),
 ('"S100A2"', 2165.2474226804125),
 ('"F3"', 2160.2474226804125),
 ('"IGFBP3"', 2055.3092783505153),
 ('"MT-CYB"', 2020.8969072164948),
 ('"HMGA1"', 1983.9072164948454),
 ('"GPRC5A"', 1980.1855670103093),
 ('"UBB"', 1974.5463917525774),
 ('"MT-CO3"', 1969.9484536082475),
 ('"FDFT1"', 1949.8041237113403),
 ('"SLC3A2"', 1931.4742268041236),
 ('"MT-ND4L"', 1908.9690721649486),
 ('"KRT6A"', 1851.3711340206185),
 ('"LDHB"', 1846.7113402061855),
 ('"TXN"', 1813.7731958762886),
 ('"PLIN2"', 1804.4845360824743),
 ('"GPX3"', 1768.5051546391753),
 ('"DHCR24"', 1761.3917525773195),
 ('"MALAT1"', 1684.2268041237114),
 ('"UCA1"', 1673.7319587628865),
 ('"TACSTD2"', 1649.1237113402062),
 ('"FAM83A"', 1618.639175257732),
 ('"DHCR7"', 1582.1649484536083),
 ('"H2AZ1"', 1576.5979381443299),
 ('"STMN1"', 1556.4948453608247),
 ('"ITGA6"', 1545.701030927835),
 ('"PHLDA1"', 1526.4020618556701),
 ('"ADIRF-AS1"', 1519.4432989690722),
 ('"CSTB"', 1507.041237113402),
 ('"ADIRF"', 1475.7525773195875),
 ('"BLCAP"', 1404.8041237113403),
 ('"KRT17"', 1387.6494845360826),
 ('"HSP90AA1"', 1384.5051546391753),
 ('"TINAGL1"', 1374.319587628866),
 ('"ACTN1"', 1358.8453608247423),
 ('"KRT13"', 1356.721649484536),
 ('"JUP"', 1350.1443298969073),
 ('"S100A16"', 1340.4329896907216),
 ('"FOS"', 1338.8041237113403),
 ('"GAS6"', 1286.9278350515465),
 ('"TMBIM1"', 1281.1958762886597),
 ('"FOSL1"', 1265.6907216494844),
 ('"SFN"', 1225.3298969072166),
 ('"TUBB4B"', 1219.0515463917525),
 ('"UPK1B"', 1190.4742268041236),
 ('"ERO1A"', 1181.680412371134),
 ('"PXN"', 1180.5257731958764),
 ('"SQLE"', 1178.2474226804125),
 ('"S100A6"', 1133.5567010309278),
 ('"SQSTM1"', 1132.6907216494844),
 ('"SLC1A5"', 1121.6082474226805),
 ('"MT2A"', 1117.319587628866),
 ('"PERP"', 1113.701030927835),
 ('"BNIP3"', 1110.979381443299),
 ('"KRT5"', 1106.3917525773195),
 ('"MYH9"', 1104.0927835051546),
 ('"SERPINB5"', 1102.7835051546392),
 ('"LAMA3"', 1097.4742268041236),
 ('"FSCN1"', 1090.2061855670104),
 ('"GLUL"', 1079.9484536082475),
 ('"TFRC"', 1076.2474226804125),
 ('"COL17A1"', 1024.2886597938145),
 ('"NQO1"', 1021.4226804123712),
 ('"SEC61A1"', 1017.1443298969073),
 ('"ACAT2"', 1016.4742268041238),
 ('"CTSD"', 1010.4123711340206),
 ('"GRN"', 1010.0927835051547),
 ('"MAL2"', 1003.4536082474227),
 ('"IDI1"', 989.5876288659794),
 ('"JUNB"', 988.7731958762887),
 ('"KPNA2"', 986.7010309278351),
 ('"SLC38A2"', 980.680412371134),
 ('"FLNB"', 976.5360824742268),
 ('"KRT14"', 965.3092783505155),
 ('"CDC20"', 960.4536082474227),
 ('"RRM2"', 958.319587628866),
 ('"ST14"', 943.2268041237113),
 ('"AKR1C2"', 940.5360824742268),
 ('"FLNA"', 937.2577319587629),
 ('"HLA-A"', 927.3092783505155),
 ('"FXYD3"', 907.8247422680413),
 ('"KDM1A"', 900.7835051546392),
 ('"SLC25A1"', 898.9175257731958),
 ('"TUBB6"', 898.8659793814433),
 ('"ITGB4"', 887.1649484536083),
 ('"FGFBP1"', 883.6082474226804),
 ('"CAVIN3"', 873.2164948453608),
 ('"G0S2"', 873.1649484536083),
 ('"PRNP"', 871.6288659793814),
 ('"PLAU"', 864.9381443298969),
 ('"H4C3"', 852.4020618556701),
 ('"NDRG1"', 830.7525773195877),
 ('"P4HA1"', 824.0412371134021),
 ('"NFKBIA"', 819.340206185567),
 ('"IRF6"', 818.1134020618557),
 ('"MCM3"', 809.659793814433),
 ('"PLK2"', 804.4536082474227),
 ('"PTHLH"', 794.0103092783505),
 ('"TIMP3"', 793.4845360824743),
 ('"LAMC1"', 784.1237113402062),
 ('"PHLDA1-AS1"', 782.4536082474227),
 ('"AMIGO2"', 780.8556701030927),
 ('"ZFP36L1"', 779.0618556701031),
 ('"SLCO4A1"', 777.6907216494845),
 ('"TPX2"', 776.0309278350516),
 ('"CDKN1A"', 774.1134020618557),
 ('"BHLHE40"', 764.3298969072165),
 ('"CDK2AP2"', 754.2886597938144),
 ('"SLC7A5"', 739.8865979381443),
 ('"HK1"', 735.7422680412371),
 ('"SEMA4B"', 731.659793814433),
 ('"TXNRD1"', 724.0927835051547),
 ('"PYGB"', 719.9175257731958),
 ('"TPBG"', 712.7422680412371),
 ('"PCDH1"', 704.7731958762887),
 ('"MT-RNR1"', 696.0515463917526),
 ('"PFKFB3"', 687.9072164948453),
 ('"AHNAK"', 687.659793814433),
 ('"CLDN4"', 673.7835051546392),
 ('"LIPA"', 670.9587628865979),
 ('"DSP"', 668.1649484536083),
 ('"ALDOC"', 666.319587628866),
 ('"COPG1"', 656.4536082474227),
 ('"WARS1"', 655.7113402061856),
 ('"NET1"', 651.0309278350516),
 ('"HSPH1"', 649.2474226804123),
 ('"AKR1B1"', 645.2268041237113),
 ('"THBS1"', 638.8865979381443),
 ('"HERPUD1"', 636.6082474226804),
 ('"INSIG1"', 633.9587628865979),
 ('"IER2"', 628.1649484536083),
 ('"ARL6IP1"', 624.7835051546392),
 ('"SLC6A8"', 623.8350515463917),
 ('"DUSP1"', 613.5979381443299),
 ('"CPA4"', 613.1958762886597),
 ('"LDLR"', 607.1134020618557),
 ('"BIRC5"', 602.1958762886597),
 ('"C19orf48"', 599.5154639175257),
 ('"PFKL"', 598.5876288659794),
 ('"POLR2A"', 591.9690721649484),
 ('"MCM7"', 591.3814432989691),
 ('"HMGB2"', 591.3711340206186),
 ('"PPP1R15A"', 586.4742268041238),
 ('"EHF"', 584.1134020618557),
 ('"DUSP6"', 582.7938144329897),
 ('"CDH1"', 581.2164948453608),
 ('"CALB1"', 570.8556701030927),
 ('"FJX1"', 568.9484536082474),
 ('"GPX2"', 568.7422680412371),
 ('"CCNB1"', 564.9587628865979),
 ('"AKR1C3"', 564.9175257731958),
 ('"MSMO1"', 562.4226804123712),
 ('"FAM162A"', 560.7525773195877),
 ('"TRIB3"', 559.0721649484536),
 ('"KRT4"', 556.7216494845361),
 ('"MCM4"', 552.9381443298969),
 ('"TRIM29"', 544.9484536082474),
 ('"MEST"', 539.979381443299),
 ('"DKK1"', 538.8556701030927),
 ('"POP4"', 535.1958762886597),
 ('"SPP1"', 532.8659793814433),
 ('"UGT1A10"', 531.5463917525773),
 ('"NUSAP1"', 525.1134020618557),
 ('"PRSS23"', 520.1237113402062),
 ('"UGT1A6"', 516.3298969072165),
 ('"AURKB"', 510.41237113402065),
 ('"MET"', 510.1855670103093),
 ('"ATAD2"', 508.77319587628864),
 ('"ARNTL2"', 506.49484536082474),
 ('"CAVIN1"', 505.5979381443299),
 ('"MRNIP"', 501.5670103092784),
 ('"EIF5"', 500.08247422680415),
 ('"GLTP"', 499.4020618556701),
 ('"CLDN7"', 497.03092783505156),
 ('"AARS1"', 495.8556701030928),
 ('"PHGDH"', 493.92783505154637),
 ('"HMGCR"', 488.70103092783506),
 ('"PTP4A1"', 486.44329896907215),
 ('"ARRDC3"', 485.1340206185567),
 ('"SRXN1"', 479.7525773195876),
 ('"CALML5"', 477.7628865979381),
 ('"ID1"', 477.1546391752577),
 ('"MCM2"', 475.25773195876286),
 ('"PSAT1"', 468.659793814433),
 ('"SERPINE1"', 464.12371134020617),
 ('"BRD2"', 463.36082474226805),
 ('"GFPT1"', 459.07216494845363),
 ('"SDC1"', 456.30927835051546),
 ('"EIF4A2"', 448.11340206185565),
 ('"SRM"', 445.12371134020617),
 ('"ITGA5"', 443.07216494845363),
 ('"MYBL2"', 441.1855670103093),
 ('"IRAK1"', 440.1855670103093),
 ('"SLC20A1"', 439.0515463917526),
 ('"TSC22D1"', 437.8144329896907),
 ('"NUCB2"', 436.70103092783506),
 ('"HSPB1"', 433.2474226804124),
 ('"UBE2C"', 432.87628865979383),
 ('"C4orf3"', 432.6701030927835),
 ('"CDC6"', 432.50515463917526),
 ('"BNIP3L"', 427.74226804123714),
 ('"PPARG"', 427.5979381443299),
 ('"ZWINT"', 423.50515463917526),
 ('"UAP1"', 421.9072164948454),
 ('"ANLN"', 420.6701030927835),
 ('"NCAPD2"', 420.54639175257734),
 ('"KLK10"', 415.0),
 ('"MKI67"', 413.02061855670104),
 ('"KLF6"', 411.9587628865979),
 ('"MCM5"', 410.36082474226805),
 ('"FEN1"', 408.74226804123714),
 ('"RNF26"', 408.340206185567),
 ('"EFEMP1"', 407.9896907216495),
 ('"GYS1"', 406.0618556701031),
 ('"MSH6"', 405.2680412371134),
 ('"KDM5B"', 405.07216494845363),
 ('"HES1"', 403.680412371134),
 ('"EGLN3"', 398.70103092783506),
 ('"CYP51A1"', 395.5670103092784),
 ('"TRIM16"', 395.340206185567),
 ('"ERRFI1"', 393.9484536082474),
 ('"CTNNAL1"', 393.6082474226804),
 ('"RBM14"', 392.8144329896907),
 ('"NOLC1"', 391.07216494845363),
 ('"FN1"', 385.1546391752577),
 ('"STC2"', 384.5670103092784),
 ('"P4HA2"', 384.2371134020619),
 ('"ADM"', 383.3298969072165),
 ('"PRC1"', 374.2886597938144),
 ('"UNG"', 374.2783505154639),
 ('"PCNA"', 374.1958762886598),
 ('"BCAR1"', 374.0618556701031),
 ('"PTTG1"', 373.340206185567),
 ('"CDK1"', 373.2371134020619),
 ('"PLOD2"', 371.5360824742268),
 ('"TOP2A"', 366.69072164948454),
 ('"LRRC8A"', 363.4845360824742),
 ('"ID3"', 362.680412371134),
 ('"DCBLD2"', 361.5257731958763),
 ('"VEGFA"', 358.0103092783505),
 ('"CCNB2"', 354.0927835051546),
 ('"NUP188"', 350.74226804123714),
 ('"ABLIM1"', 350.3917525773196),
 ('"CA9"', 349.5257731958763),
 ('"FADS1"', 349.37113402061857),
 ('"KRT16"', 348.0618556701031),
 ('"FOSL2"', 345.91752577319585),
 ('"SSR3"', 343.17525773195877),
 ('"BAG3"', 342.6701030927835),
 ('"EGLN1"', 342.3814432989691),
 ('"UGDH"', 341.659793814433),
 ('"MMP1"', 341.55670103092785),
 ('"COL4A2"', 337.77319587628864),
 ('"PINK1"', 337.5257731958763),
 ('"CEACAM5"', 335.70103092783506),
 ('"EMP1"', 334.08247422680415),
 ('"CRKL"', 330.340206185567),
 ('"UBE2S"', 329.7216494845361),
 ('"ISG15"', 327.4329896907216),
 ('"EPHA2"', 326.77319587628864),
 ('"LSS"', 326.1443298969072),
 ('"LINC01764"', 325.58762886597935),
 ('"AVPI1"', 324.91752577319585),
 ('"KIF20A"', 321.9896907216495),
 ('"AMOTL2"', 318.4020618556701),
 ('"KIF22"', 316.77319587628864),
 ('"TOB1"', 315.37113402061857),
 ('"TGFBI"', 312.78350515463916),
 ('"LOXL2"', 312.3814432989691),
 ('"POLD1"', 311.11340206185565),
 ('"SAT1"', 310.7938144329897),
 ('"PXDN"', 308.9896907216495),
 ('"ANXA3"', 305.74226804123714),
 ('"SERPINE2"', 304.1855670103093),
 ('"H1-0"', 302.1855670103093),
 ('"PCSK9"', 301.2474226804124),
 ('"DLK2"', 299.08247422680415),
 ('"HILPDA"', 298.62886597938143),
 ('"DTL"', 298.0927835051546),
 ('"VARS1"', 297.2474226804124),
 ('"GAS6-AS1"', 294.36082474226805),
 ('"DUSP10"', 293.2886597938144),
 ('"ITGB5"', 292.96907216494844),
 ('"KIAA0040"', 292.29896907216494),
 ('"TNFRSF21"', 286.70103092783506),
 ('"KCTD11"', 286.16494845360825),
 ('"C1orf116"', 282.87628865979383),
 ('"UBE2T"', 280.4742268041237),
 ('"RND3"', 278.41237113402065),
 ('"DHRS3"', 277.5773195876289),
 ('"VSIR"', 273.7525773195876),
 ('"MVD"', 273.6701030927835),
 ('"KLF10"', 272.3298969072165),
 ('"SREBF2"', 271.9381443298969),
 ('"BCAR3"', 270.55670103092785),
 ('"SNAI2"', 268.96907216494844),
 ('"TNFAIP2"', 268.55670103092785),
 ('"TM4SF1"', 268.0515463917526),
 ('"LVRN"', 264.91752577319585),
 ('"PDLIM2"', 262.44329896907215),
 ('"MYC"', 261.2783505154639),
 ('"FABP5"', 260.10309278350513),
 ('"IL20RB"', 259.8041237113402),
 ('"PPIF"', 258.17525773195877),
 ('"TCF19"', 254.27835051546393),
 ('"ALDH3A1"', 253.41237113402062),
 ('"OAS1"', 246.1237113402062),
 ('"ACSS2"', 243.7422680412371),
 ('"SPAG5"', 242.83505154639175),
 ('"MT1X"', 240.9278350515464),
 ('"AJUBA"', 240.7319587628866),
 ('"SMG7"', 239.43298969072166),
 ('"AXL"', 238.57731958762886),
 ('"TGFBR2"', 238.51546391752578),
 ('"PPRC1"', 236.88659793814432),
 ('"ALDH1A3"', 236.7835051546392),
 ('"SMURF2"', 236.4536082474227),
 ('"HMGCS1"', 236.43298969072166),
 ('"NEU1"', 234.04123711340208),
 ('"MCM6"', 233.63917525773195),
 ('"ITGA2"', 232.23711340206185),
 ('"TXNIP"', 231.1340206185567),
 ('"MCM10"', 228.7835051546392),
 ('"TMEM200A"', 227.51546391752578),
 ('"PTGES"', 227.4536082474227),
 ('"PLEKHF1"', 224.36082474226805),
 ('"RIPK4"', 223.340206185567),
 ('"UHRF1"', 222.23711340206185),
 ('"CDH13"', 219.64948453608247),
 ('"MXD1"', 217.09278350515464),
 ('"EGR1"', 216.5257731958763),
 ('"GPNMB"', 216.30927835051546),
 ('"LY6D"', 213.51546391752578),
 ('"KNSTRN"', 213.35051546391753),
 ('"MOB3A"', 212.4020618556701),
 ('"FUT11"', 212.16494845360825),
 ('"FST"', 209.1340206185567),
 ('"SEC24D"', 206.70103092783506),
 ('"KIF23"', 205.20618556701032),
 ('"RBCK1"', 203.94845360824743),
 ('"NID2"', 202.28865979381445),
 ('"KIF2C"', 201.8041237113402),
 ('"CKS2"', 201.36082474226805),
 ('"TNFAIP3"', 201.0),
 ('"XDH"', 200.84536082474227),
 ('"GINS2"', 200.02061855670104),
 ('"KLK5"', 199.659793814433),
 ('"SLC37A2"', 199.09278350515464),
 ('"SYTL3"', 198.44329896907217),
 ('"AHNAK2"', 198.17525773195877),
 ('"FSTL1"', 197.79381443298968),
 ('"HAS2"', 197.56701030927834),
 ('"CLDN1"', 197.53608247422682),
 ('"ASF1B"', 196.02061855670104),
 ('"LGALS1"', 195.27835051546393),
 ('"CD74"', 195.02061855670104),
 ('"KIF11"', 194.55670103092783),
 ('"MELK"', 194.53608247422682),
 ('"CCNE1"', 194.43298969072166),
 ('"HK2"', 193.39175257731958),
 ('"CCNA2"', 192.46391752577318),
 ('"CDC45"', 191.01030927835052),
 ('"LIG1"', 190.5979381443299),
 ('"YPEL5"', 189.42268041237114),
 ('"CRIP2"', 189.11340206185568),
 ('"FAM111A"', 187.01030927835052),
 ('"PPL"', 186.5463917525773),
 ('"KCNMA1"', 186.17525773195877),
 ('"ANO1"', 186.08247422680412),
 ('"NECTIN2"', 185.7422680412371),
 ('"RFWD3"', 185.3298969072165),
 ('"WNT10A"', 185.1443298969072),
 ('"CEP55"', 184.77319587628867),
 ('"SLPI"', 182.79381443298968),
 ('"AKR1C1"', 181.49484536082474),
 ('"TMEM45A"', 181.28865979381445),
 ('"SLC43A3"', 181.16494845360825),
 ('"NRP1"', 181.06185567010309)]
In [ ]:
normoxia_samples_summary = normoxia_samples.describe()

normo_genes = normoxia_samples_summary.loc['mean'][normoxia_samples_summary.loc['mean'] > 152.21]
normo_genes_sorted = sorted(normo_genes.items(), key=lambda x: x[1], reverse=True)
normo_genes_sorted # 538 genes overcome the mean threshold
Out[ ]:
[('"FTL"', 25689.24705882353),
 ('"CD44"', 11510.24705882353),
 ('"FTH1"', 11339.929411764706),
 ('"BEST1"', 9879.823529411764),
 ('"LDHA"', 8955.69411764706),
 ('"HSPA5"', 8251.588235294117),
 ('"MT-RNR2"', 7889.894117647059),
 ('"KRT19"', 6903.270588235294),
 ('"PSMD2"', 6743.4),
 ('"KRT18"', 6273.070588235294),
 ('"LAMC2"', 5719.164705882353),
 ('"MT-ND4"', 5480.964705882353),
 ('"LAMB3"', 5360.411764705882),
 ('"CD59"', 5279.7764705882355),
 ('"B2M"', 5247.623529411765),
 ('"MT-CO2"', 5157.517647058824),
 ('"ALDOA"', 4542.941176470588),
 ('"KRT8"', 4476.529411764706),
 ('"HSPA8"', 4437.917647058824),
 ('"HSP90B1"', 4296.529411764706),
 ('"ACTG1"', 3791.9882352941177),
 ('"CALM2"', 3691.247058823529),
 ('"PRDX1"', 3339.035294117647),
 ('"TUBB"', 3261.235294117647),
 ('"ANXA1"', 3196.470588235294),
 ('"UBC"', 3171.294117647059),
 ('"EZR"', 3146.1529411764704),
 ('"TUBA1B"', 3101.3176470588237),
 ('"SDHA"', 3089.3058823529414),
 ('"TMSB10"', 2957.329411764706),
 ('"LDHB"', 2859.4470588235295),
 ('"S100A2"', 2763.2705882352943),
 ('"TMSB4X"', 2710.9058823529413),
 ('"MT-CYB"', 2700.8117647058825),
 ('"UBB"', 2684.764705882353),
 ('"CAV1"', 2610.2),
 ('"MT-ND5"', 2599.858823529412),
 ('"MT-CO3"', 2563.258823529412),
 ('"TXN"', 2483.235294117647),
 ('"HMGA1"', 2434.3529411764707),
 ('"KRT7"', 2390.0235294117647),
 ('"MIF"', 2386.858823529412),
 ('"MIF-AS1"', 2380.3058823529414),
 ('"LMNA"', 2293.3058823529414),
 ('"CTSB"', 2261.2823529411767),
 ('"H2AZ1"', 2040.6941176470589),
 ('"MT-ND4L"', 2000.6941176470589),
 ('"TK1"', 1998.9882352941177),
 ('"PSAP"', 1984.835294117647),
 ('"SLC3A2"', 1975.5176470588235),
 ('"AKR1C2"', 1941.1882352941177),
 ('"P4HB"', 1887.1764705882354),
 ('"HSP90AA1"', 1799.0117647058823),
 ('"GPX3"', 1766.8235294117646),
 ('"CANX"', 1706.3882352941177),
 ('"NQO1"', 1665.8117647058823),
 ('"MALAT1"', 1651.2588235294118),
 ('"KRT6A"', 1590.5176470588235),
 ('"FOSL1"', 1576.9764705882353),
 ('"CDC20"', 1556.0235294117647),
 ('"TFRC"', 1550.7882352941176),
 ('"TUBB4B"', 1540.5058823529412),
 ('"STMN1"', 1510.929411764706),
 ('"KPNA2"', 1509.8588235294117),
 ('"SQSTM1"', 1504.2117647058824),
 ('"S100A16"', 1481.9529411764706),
 ('"PHLDA1"', 1457.5529411764705),
 ('"ACTN1"', 1437.270588235294),
 ('"PGK1"', 1430.070588235294),
 ('"F3"', 1409.3882352941177),
 ('"GAS6"', 1359.0470588235294),
 ('"AKR1C3"', 1334.3647058823528),
 ('"PLAU"', 1332.1529411764707),
 ('"ITGA6"', 1325.270588235294),
 ('"ADIRF-AS1"', 1314.2823529411764),
 ('"ADIRF"', 1274.035294117647),
 ('"H4C3"', 1232.2588235294118),
 ('"CCNB1"', 1211.870588235294),
 ('"GPI"', 1189.8823529411766),
 ('"SFN"', 1177.4470588235295),
 ('"EIF5"', 1170.6588235294118),
 ('"SCD"', 1169.9176470588236),
 ('"DHCR24"', 1160.8470588235293),
 ('"PXN"', 1155.129411764706),
 ('"SPP1"', 1141.2235294117647),
 ('"PRNP"', 1136.6705882352942),
 ('"FDFT1"', 1132.9058823529413),
 ('"SERPINB5"', 1122.129411764706),
 ('"MAL2"', 1116.7529411764706),
 ('"SEC61A1"', 1115.9176470588236),
 ('"LAMA3"', 1108.2823529411764),
 ('"KRT5"', 1079.7058823529412),
 ('"MT2A"', 1073.9529411764706),
 ('"TPX2"', 1071.2117647058824),
 ('"RRM2"', 1069.3647058823528),
 ('"GLUL"', 1050.2470588235294),
 ('"DKK1"', 1024.0823529411764),
 ('"GPRC5A"', 1017.7882352941176),
 ('"PLK2"', 997.7529411764706),
 ('"TACSTD2"', 991.7411764705882),
 ('"SLC1A5"', 989.4),
 ('"SRXN1"', 957.2823529411764),
 ('"PERP"', 945.8),
 ('"HSPH1"', 942.0470588235294),
 ('"S100A6"', 921.0588235294117),
 ('"MT-RNR1"', 917.8705882352941),
 ('"TINAGL1"', 914.0823529411765),
 ('"THBS1"', 888.1882352941177),
 ('"TXNRD1"', 871.5764705882353),
 ('"KRT14"', 871.0941176470589),
 ('"MCM3"', 867.4117647058823),
 ('"COL17A1"', 858.2823529411764),
 ('"DHCR7"', 840.1764705882352),
 ('"SRM"', 838.564705882353),
 ('"PYGB"', 834.8),
 ('"BIRC5"', 828.8705882352941),
 ('"C19orf48"', 827.435294117647),
 ('"GPX2"', 823.1058823529412),
 ('"JUP"', 812.2235294117647),
 ('"UCA1"', 802.7882352941176),
 ('"NFKBIA"', 791.8470588235294),
 ('"FLNB"', 786.8470588235294),
 ('"KDM1A"', 781.6823529411764),
 ('"LAMC1"', 779.9882352941177),
 ('"TUBB6"', 779.7764705882353),
 ('"PTHLH"', 773.5411764705882),
 ('"FLNA"', 773.4823529411765),
 ('"ACAT2"', 773.1764705882352),
 ('"MYH9"', 762.1764705882352),
 ('"HMGB2"', 754.7176470588236),
 ('"ST14"', 753.364705882353),
 ('"PHLDA1-AS1"', 742.0941176470589),
 ('"FJX1"', 737.9764705882353),
 ('"POP4"', 737.4),
 ('"KRT17"', 736.8),
 ('"CSTB"', 700.5058823529412),
 ('"GRN"', 697.035294117647),
 ('"MMP1"', 695.3764705882353),
 ('"MCM7"', 683.9294117647058),
 ('"AKR1B1"', 682.435294117647),
 ('"BLCAP"', 668.7411764705882),
 ('"AURKB"', 668.5058823529412),
 ('"MRNIP"', 665.5411764705882),
 ('"NUSAP1"', 656.7647058823529),
 ('"WARS1"', 656.6),
 ('"ZWINT"', 653.7647058823529),
 ('"LIPA"', 649.2117647058824),
 ('"SLC20A1"', 639.5411764705882),
 ('"NOLC1"', 637.5058823529412),
 ('"AKR1C1"', 636.0823529411765),
 ('"ARL6IP1"', 631.7529411764706),
 ('"PRSS23"', 631.035294117647),
 ('"SLC7A5"', 626.6470588235294),
 ('"CPA4"', 615.8588235294118),
 ('"EHF"', 614.0117647058823),
 ('"HLA-A"', 610.4470588235295),
 ('"FSCN1"', 604.0823529411765),
 ('"CDC6"', 595.1882352941177),
 ('"G0S2"', 594.635294117647),
 ('"JUNB"', 594.4823529411765),
 ('"SLC2A1"', 590.6823529411764),
 ('"PCDH1"', 578.0),
 ('"NET1"', 576.2705882352941),
 ('"CAVIN3"', 571.7294117647059),
 ('"MEST"', 570.6588235294117),
 ('"ATAD2"', 569.2941176470588),
 ('"SLC38A2"', 567.3176470588236),
 ('"TMBIM1"', 563.2705882352941),
 ('"PRC1"', 563.2),
 ('"SLC25A1"', 556.3764705882353),
 ('"ARNTL2"', 548.2941176470588),
 ('"IRF6"', 546.5294117647059),
 ('"UGDH"', 538.6117647058824),
 ('"FEN1"', 538.364705882353),
 ('"FXYD3"', 537.6588235294117),
 ('"PTP4A1"', 536.4705882352941),
 ('"ITGB4"', 529.8235294117648),
 ('"DDIT4"', 529.0117647058823),
 ('"MCM2"', 529.0),
 ('"IDI1"', 526.2588235294118),
 ('"PSAT1"', 526.2470588235294),
 ('"COPG1"', 524.7294117647059),
 ('"AMIGO2"', 512.635294117647),
 ('"TRIM16"', 507.90588235294115),
 ('"CTSD"', 502.9529411764706),
 ('"SQLE"', 500.6235294117647),
 ('"FAM83A"', 500.0705882352941),
 ('"CTNNAL1"', 499.94117647058823),
 ('"UBE2S"', 498.4823529411765),
 ('"MCM4"', 497.5764705882353),
 ('"ZFP36L1"', 497.45882352941175),
 ('"CCNB2"', 491.5176470588235),
 ('"UPK1B"', 491.05882352941177),
 ('"PCNA"', 487.49411764705883),
 ('"ID3"', 487.08235294117645),
 ('"FGFBP1"', 485.1411764705882),
 ('"MYBL2"', 478.52941176470586),
 ('"MKI67"', 476.5764705882353),
 ('"NUP188"', 475.4),
 ('"UBE2C"', 461.2470588235294),
 ('"UAP1"', 458.9529411764706),
 ('"CDK2AP2"', 457.0352941176471),
 ('"ANLN"', 453.4470588235294),
 ('"GFPT1"', 451.7294117647059),
 ('"POLR2A"', 450.21176470588233),
 ('"UGT1A6"', 450.01176470588234),
 ('"BAG3"', 447.29411764705884),
 ('"DCBLD2"', 447.21176470588233),
 ('"UGT1A10"', 446.3764705882353),
 ('"PTTG1"', 443.0),
 ('"DTL"', 439.4235294117647),
 ('"MET"', 437.5529411764706),
 ('"IER2"', 432.0),
 ('"UBE2T"', 423.38823529411764),
 ('"HK1"', 422.29411764705884),
 ('"KIF20A"', 421.36470588235295),
 ('"RNF26"', 415.4235294117647),
 ('"MCM5"', 413.98823529411766),
 ('"MSH6"', 413.1058823529412),
 ('"ID1"', 410.08235294117645),
 ('"CCNE1"', 408.74117647058824),
 ('"KIF23"', 408.7294117647059),
 ('"DSP"', 407.21176470588233),
 ('"IRAK1"', 404.7647058823529),
 ('"PPP1R15A"', 404.4470588235294),
 ('"CDKN1A"', 403.9529411764706),
 ('"TIMP3"', 399.98823529411766),
 ('"EFEMP1"', 393.52941176470586),
 ('"AURKA"', 393.2823529411765),
 ('"CDK1"', 393.15294117647056),
 ('"FOS"', 392.2705882352941),
 ('"SLC7A11"', 388.67058823529413),
 ('"BRD2"', 386.4470588235294),
 ('"RBM14"', 386.21176470588233),
 ('"CLDN4"', 385.1058823529412),
 ('"PPIF"', 385.05882352941177),
 ('"NCAPD2"', 383.8941176470588),
 ('"EPHA2"', 382.47058823529414),
 ('"AVPI1"', 380.15294117647056),
 ('"KNSTRN"', 378.4470588235294),
 ('"TRIM29"', 378.3764705882353),
 ('"CLDN7"', 378.2),
 ('"TRIB3"', 377.1294117647059),
 ('"IGFBP3"', 376.56470588235294),
 ('"KRT13"', 374.56470588235294),
 ('"PHGDH"', 372.45882352941175),
 ('"AARS1"', 372.0705882352941),
 ('"UNG"', 369.2470588235294),
 ('"KIF22"', 368.4470588235294),
 ('"LDLR"', 367.0),
 ('"MYC"', 365.25882352941176),
 ('"LRRC8A"', 365.15294117647056),
 ('"SEMA4B"', 364.2),
 ('"VARS1"', 361.9647058823529),
 ('"KIAA0040"', 361.6588235294118),
 ('"MCM10"', 361.3764705882353),
 ('"TOP2A"', 360.36470588235295),
 ('"ALDH1A3"', 356.38823529411764),
 ('"ANXA3"', 351.22352941176473),
 ('"KYNU"', 350.54117647058825),
 ('"CAVIN1"', 349.22352941176473),
 ('"SLCO4A1"', 344.8235294117647),
 ('"DUSP6"', 344.1411764705882),
 ('"SPAG5"', 339.0),
 ('"CDH1"', 337.2),
 ('"PFKL"', 336.0705882352941),
 ('"ERO1A"', 332.4823529411765),
 ('"CCNA2"', 330.9294117647059),
 ('"ABLIM1"', 326.6588235294118),
 ('"KIF2C"', 324.98823529411766),
 ('"SSR3"', 322.45882352941175),
 ('"BUB1B"', 320.1764705882353),
 ('"PLIN2"', 320.1411764705882),
 ('"AHNAK"', 317.8235294117647),
 ('"EIF4A2"', 315.50588235294117),
 ('"EMP1"', 314.4117647058824),
 ('"SERPINE2"', 312.6470588235294),
 ('"TNFRSF21"', 312.01176470588234),
 ('"TGFBI"', 308.50588235294117),
 ('"CKS2"', 307.8588235294118),
 ('"CALML5"', 307.3529411764706),
 ('"SMG7"', 307.3176470588235),
 ('"PLK1"', 306.0705882352941),
 ('"HERPUD1"', 304.88235294117646),
 ('"GAS6-AS1"', 303.4235294117647),
 ('"NUCB2"', 302.98823529411766),
 ('"CALB1"', 302.78823529411767),
 ('"ISG15"', 299.43529411764706),
 ('"TPBG"', 295.43529411764706),
 ('"POLD1"', 293.9764705882353),
 ('"RND3"', 292.6),
 ('"ALDH3A1"', 288.25882352941176),
 ('"GINS2"', 283.11764705882354),
 ('"CDC45"', 281.21176470588233),
 ('"SDC1"', 279.2705882352941),
 ('"COL4A2"', 278.3176470588235),
 ('"C10orf55"', 268.9294117647059),
 ('"FST"', 264.05882352941177),
 ('"CRKL"', 263.5529411764706),
 ('"NRP1"', 262.94117647058823),
 ('"PTGES"', 260.8705882352941),
 ('"PLEKHF1"', 252.95294117647057),
 ('"PPRC1"', 252.25882352941176),
 ('"ASF1B"', 249.0705882352941),
 ('"FAM83D"', 248.61176470588236),
 ('"CKAP2"', 248.4),
 ('"SAT1"', 247.28235294117647),
 ('"TGFBR2"', 243.8),
 ('"ITGB5"', 243.08235294117648),
 ('"MSMO1"', 241.75294117647059),
 ('"SHCBP1"', 240.72941176470587),
 ('"HJURP"', 240.2941176470588),
 ('"BCAR3"', 238.6705882352941),
 ('"BCAR1"', 236.7058823529412),
 ('"LVRN"', 234.90588235294118),
 ('"LIG1"', 234.4),
 ('"MELK"', 233.30588235294118),
 ('"CDKN3"', 233.25882352941176),
 ('"KIF11"', 232.88235294117646),
 ('"SNAI2"', 230.63529411764705),
 ('"UHRF1"', 230.52941176470588),
 ('"CEP55"', 229.38823529411764),
 ('"ITGA5"', 228.61176470588236),
 ('"KLF6"', 228.0),
 ('"BUB1"', 227.1529411764706),
 ('"DUSP1"', 224.76470588235293),
 ('"RFWD3"', 224.63529411764705),
 ('"TNFAIP2"', 222.95294117647057),
 ('"HSPB1"', 220.83529411764707),
 ('"MCM6"', 220.57647058823528),
 ('"GPNMB"', 219.63529411764705),
 ('"KLK5"', 217.31764705882352),
 ('"ANO1"', 216.98823529411766),
 ('"IL20RB"', 215.43529411764706),
 ('"PIMREG"', 214.95294117647057),
 ('"FSTL1"', 214.94117647058823),
 ('"PHF19"', 214.16470588235293),
 ('"KRT4"', 213.95294117647057),
 ('"AJUBA"', 213.88235294117646),
 ('"TMEM200A"', 212.5529411764706),
 ('"TOB1"', 211.47058823529412),
 ('"KLK10"', 211.43529411764706),
 ('"CCNE2"', 209.9294117647059),
 ('"PLAT"', 209.51764705882354),
 ('"CD55"', 206.8),
 ('"RRP12"', 206.05882352941177),
 ('"NCAPG"', 205.61176470588236),
 ('"PINK1"', 204.94117647058823),
 ('"AMOTL2"', 204.42352941176472),
 ('"CDCA8"', 204.23529411764707),
 ('"SMURF2"', 204.16470588235293),
 ('"HAS2"', 202.27058823529413),
 ('"FAM111A"', 202.09411764705882),
 ('"XDH"', 201.8235294117647),
 ('"PLAUR"', 200.81176470588235),
 ('"TM4SF1"', 200.8),
 ('"ALDH1A3-AS1"', 200.74117647058824),
 ('"ECT2"', 199.8235294117647),
 ('"TSC22D1"', 196.88235294117646),
 ('"TCF19"', 196.8235294117647),
 ('"ITGA2"', 196.35294117647058),
 ('"CDH13"', 195.52941176470588),
 ('"CYP51A1"', 194.96470588235294),
 ('"SERPINE1"', 194.12941176470588),
 ('"NID2"', 190.58823529411765),
 ('"NDC80"', 190.5529411764706),
 ('"CENPF"', 188.95294117647057),
 ('"SYTL3"', 188.2),
 ('"SERPINB2"', 188.12941176470588),
 ('"CD74"', 187.95294117647057),
 ('"CYP1B1"', 187.72941176470587),
 ('"FADS1"', 184.54117647058823),
 ('"CENPA"', 184.08235294117648),
 ('"HMGCR"', 183.3764705882353),
 ('"EPDR1"', 182.52941176470588),
 ('"NUMA1"', 181.65882352941176),
 ('"P4HA1"', 179.23529411764707),
 ('"TYSND1"', 178.03529411764706),
 ('"EXO1"', 177.28235294117647),
 ('"CDCA3"', 177.27058823529413),
 ('"SEC24D"', 175.50588235294117),
 ('"FYB1"', 174.04705882352943),
 ('"CCNF"', 172.85882352941175),
 ('"VEGFA"', 172.5529411764706),
 ('"DUSP10"', 171.94117647058823),
 ('"GYS1"', 170.8470588235294),
 ('"LINC01764"', 170.63529411764705),
 ('"PXDN"', 170.61176470588236),
 ('"GLTP"', 168.63529411764705),
 ('"NECTIN2"', 167.6),
 ('"KLF10"', 167.2),
 ('"NEK2"', 165.63529411764705),
 ('"GADD45A"', 165.16470588235293),
 ('"FHL1"', 162.94117647058823),
 ('"FAM162A"', 162.85882352941175),
 ('"PPARG"', 162.4),
 ('"SLC25A19"', 162.2941176470588),
 ('"RECQL4"', 161.2235294117647),
 ('"DLGAP5"', 158.98823529411766),
 ('"NEU1"', 158.72941176470587),
 ('"SLC7A8"', 158.38823529411764),
 ('"LSS"', 158.1529411764706),
 ('"SLC43A3"', 157.9764705882353),
 ('"IFIT2"', 157.23529411764707),
 ('"RIPK4"', 157.1529411764706),
 ('"KIF18A"', 156.78823529411764),
 ('"RBCK1"', 156.68235294117648),
 ('"VSIR"', 155.08235294117648),
 ('"PFKFB3"', 154.04705882352943)]
In [ ]:
# Transpose the DataFrames
hypoxia_summary_t = hypoxia_summary.transpose()
normoxia_summary_t = normoxia_summary.transpose()

# Calculate the difference in means
diff_mean = abs(hypoxia_summary_t['mean'] - normoxia_summary_t['mean'])

# Sort genes by difference in mean in descending order
sorted_genes = diff_mean.sort_values(ascending=False)

# Get the list of genes
genes_list = sorted_genes.index.tolist()
genes_list

# Create a DataFrame from the sorted series
sorted_genes_df = sorted_genes.reset_index()

# Rename the columns
sorted_genes_df.columns = ['Gene', 'Difference']

# Display the DataFrame
sorted_genes_df[:50]
Out[ ]:
Gene Difference
0 "LDHA" 15099.687326
1 "KRT19" 6694.110855
2 "DDIT4" 6585.256277
3 "ALDOA" 5756.728927
4 "FTL" 4227.133657
5 "PGK1" 3859.702608
6 "FTH1" 2527.503578
7 "ANGPTL4" 2214.859673
8 "BEST1" 2189.887811
9 "HSP90B1" 2002.769557
10 "HSPA5" 1991.525167
11 "SLC2A1" 1866.286719
12 "KRT18" 1687.926258
13 "CD44" 1683.999636
14 "IGFBP3" 1678.744572
15 "TMSB10" 1646.371619
16 "SCD" 1569.659673
17 "PLIN2" 1484.343360
18 "GPI" 1432.890843
19 "HSPA8" 1394.031049
20 "PSMD2" 1366.606186
21 "MIF" 1243.419527
22 "MIF-AS1" 1239.786901
23 "UBC" 1205.241965
24 "FAM83A" 1118.568587
25 "LAMB3" 1072.629472
26 "LDHB" 1012.735719
27 "AKR1C2" 1000.652153
28 "BNIP3" 994.779381
29 "KRT13" 982.156944
30 "GPRC5A" 962.397332
31 "FOS" 946.533535
32 "P4HB" 875.400849
33 "UCA1" 870.943723
34 "ERO1A" 849.198059
35 "FDFT1" 816.898241
36 "CSTB" 806.535355
37 "NDRG1" 798.352577
38 "AKR1C3" 769.447180
39 "F3" 750.859187
40 "DHCR7" 741.988478
41 "BLCAP" 736.062947
42 "TMBIM1" 717.925288
43 "UBB" 710.218314
44 "UPK1B" 699.415403
45 "MT-CYB" 679.914857
46 "SQLE" 677.623893
47 "EIF5" 670.576349
48 "TXN" 669.462098
49 "BHLHE40" 657.776956
In [ ]:
## Now with the std
# Transpose the DataFrames
hypoxia_summary_t = hypoxia_summary.transpose()
normoxia_summary_t = normoxia_summary.transpose()

# Calculate the difference in means
diff_std = abs(hypoxia_summary_t['std'] - normoxia_summary_t['std'])

# Sort genes by difference in mean in descending order
sorted_genes_std = diff_std.sort_values(ascending=False)

# Get the list of genes
genes_list = sorted_genes_std.index.tolist()
genes_list

# Create a DataFrame from the sorted series
sorted_genes_df = sorted_genes_std.reset_index()

# Rename the columns
sorted_genes_df.columns = ['Gene', 'Difference']

# Display the DataFrame
sorted_genes_df[:50]
Out[ ]:
Gene Difference
0 "KRT17" 6005.431853
1 "FTL" 5824.303256
2 "FTH1" 4410.586345
3 "KRT19" 4279.691662
4 "LDHA" 4126.866651
5 "BEST1" 3882.287729
6 "KRT13" 3765.976846
7 "IGFBP3" 2896.064265
8 "KRT6A" 2894.625380
9 "DDIT4" 2540.381481
10 "HSPA5" 2122.250614
11 "ANGPTL4" 2025.504784
12 "MT-RNR2" 1879.445668
13 "KRT16" 1875.826067
14 "CEACAM5" 1874.970276
15 "KRT14" 1720.208063
16 "GLTP" 1481.139986
17 "CSTB" 1305.217655
18 "ALDOA" 1249.288517
19 "SPP1" 1246.200083
20 "PLIN2" 1240.556021
21 "AKR1C2" 1230.482103
22 "MMP1" 1189.675202
23 "KRT4" 1149.773077
24 "SLC3A2" 1139.425636
25 "KRT18" 1135.719348
26 "HSP90B1" 1110.564728
27 "PGK1" 1083.055101
28 "AKR1C3" 1066.759828
29 "FAM83A" 1060.802868
30 "JUP" 971.988308
31 "SULT2B1" 964.045317
32 "UCA1" 925.114259
33 "SCD" 911.213323
34 "UPK1B" 865.824957
35 "TMBIM1" 834.626148
36 "SLC2A1" 829.434861
37 "PRDX1" 807.772448
38 "LAMB3" 790.156340
39 "AKR1C1" 774.892078
40 "SPRR1B" 757.519035
41 "ANXA1" 701.274437
42 "GPRC5A" 679.499106
43 "HSPB1" 670.564692
44 "PSMD2" 668.017664
45 "CCNB1" 656.644089
46 "GPI" 640.737191
47 "KRT7" 622.655000
48 "UBC" 619.232557
49 "TMSB10" 610.370935
In [ ]:
#i want to do a clustering considering only genes: GAPDH, ALDOA and PGK1 as principal components
# Create a DataFrame with only the selected genes
selected_genes = ['"LDHA"', '"ALDOA"', '"PGK1"']
selected_genes_df = df_HCC1806_norm_filt.loc[selected_genes]

# Transpose the DataFrame
selected_genes_df = selected_genes_df.T

# Apply PCA to the selected genes
pca = PCA(n_components=3)
selected_genes_transformed = pca.fit_transform(selected_genes_df)

# Apply K-means clustering to the transformed data
kmeans = KMeans(n_clusters=2, random_state=42)
clusters = kmeans.fit_predict(selected_genes_transformed)

# Assign colors to conditions manually
conditions = selected_genes_df.index.to_series().apply(extract_condition)
color_map = {'Hypoxia': 'red', 'Normoxia': 'blue'}
colors = conditions.map(color_map)

# Create a 3D scatter plot
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(selected_genes_transformed[:, 0], selected_genes_transformed[:, 1], selected_genes_transformed[:, 2], c=colors)
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
plt.title('PCA of Selected Genes (LDHA, ALDOA, PGK1) Colored by Condition')
plt.show()  

c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
No description has been provided for this image
In [ ]:
# now we want to do a k-means clustering with the selected genes
# Apply K-means clustering to the transformed data
kmeans = KMeans(n_clusters=2, random_state=42)
clusters1 = kmeans.fit_predict(selected_genes_transformed)

# Assign colors to conditions manually
conditions = selected_genes_df.index.to_series().apply(extract_condition)
color_map = {'Hypoxia': 'green', 'Normoxia': 'yellow'}
colors = conditions.map(color_map)
cluster_colorsH = ['yellow' if label == 0 else 'green' for label in clusters1]

# Create a 3D scatter plot
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(selected_genes_transformed[:, 0], selected_genes_transformed[:, 1], selected_genes_transformed[:, 2], c=cluster_colorsH)
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
plt.title('K-means Clustering of Selected Genes (GAPDH, ALDOA, PGK1)')
plt.show()

#i want to see the match rate of the clustering
# Assign numeric labels based on conditions
###condition_numeric = conditions.map({'Normoxia': 0, 'Hypoxia': 1})

# Compute the confusion matrix
###cm = confusion_matrix(condition_numeric, clusters1)

# Calculate the match rate using the trace of the confusion matrix
###match_rate = np.trace(cm) / np.sum(cm)

###print(f'Match Rate: {match_rate:.2%}')

c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
No description has been provided for this image

Looking again at GAPDH, ALDOA and PGK1, we can again observe that these genes are some of the most useful in order to understand the condition of the cells. Although the result for the MCF7 cells was even more conclusive, the tests performed here show the relevance of these genes for the HCC1806 cells as well.

¶

2.2.9 More unsupervised learning

In [ ]:
X_HCC_SMART = df_trans_HCC_SMART.drop('TARGET', axis=1)
y_train_HCC_SMART=df_trans_HCC_SMART['TARGET']

scaler_HCC_SMART = StandardScaler()
X_scaled_HCC_SMART = scaler_HCC_SMART.fit_transform(X_HCC_SMART)
In [ ]:
pca2_HCC_SMART = PCA(n_components=2)
X_pca2_HCC_SMART = pca2_HCC_SMART.fit_transform(X_scaled_HCC_SMART)
In [ ]:
plt.figure(figsize=(12, 6))

plt.scatter(X_pca2_HCC_SMART[:,0][y_train_HCC_SMART==1], X_pca2_HCC_SMART[:,1][y_train_HCC_SMART==1], c='r')
plt.scatter(X_pca2_HCC_SMART[:,0][y_train_HCC_SMART==0], X_pca2_HCC_SMART[:,1][y_train_HCC_SMART==0], c='b')

plt.title("Clusterization PCA HCC_SMART")

plt.show()
No description has been provided for this image
In [ ]:
'''% of EXPLAINED VARIANCE'''
pca_HCC_SMART = PCA().fit(X_scaled_HCC_SMART)
In [ ]:
explained_variance_ratio_HCC_SMART = pca_HCC_SMART.explained_variance_ratio_
cumulative_variance_HCC_SMART = explained_variance_ratio_HCC_SMART.cumsum()

plt.plot(range(1, len(cumulative_variance_HCC_SMART) + 1), cumulative_variance_HCC_SMART, marker='o')
plt.xlabel("Number of components")
plt.ylabel("Cumulative proportion of Variance Explained")
plt.title("Analysis of explained variance")
plt.show()
No description has been provided for this image
In [ ]:
# TSNE
tsne_model_HCC_SMART = TSNE(n_components=2) #, perplexity=10
X_tsne_HCC_SMART = tsne_model_HCC_SMART.fit_transform(X_scaled_HCC_SMART)
In [ ]:
# Visualization of results
plt.figure(figsize=(12, 6))
# plt.scatter(X_train_pca[:,0], X_train_pca[:,1], cmap='viridis')


plt.scatter(X_tsne_HCC_SMART[:,0][y_train_HCC_SMART==1], X_tsne_HCC_SMART[:,1][y_train_HCC_SMART==1], c='r')
plt.scatter(X_tsne_HCC_SMART[:,0][y_train_HCC_SMART==0], X_tsne_HCC_SMART[:,1][y_train_HCC_SMART==0], c='b')

plt.title("Clusterization TSNE HCC_SMART")

plt.show()
No description has been provided for this image

¶

  1. Analysis on DropSeq and Unsupervided/Supervised learning on DropSeq

¶

3.1) MCF7

¶

3.1.0) OPENING THE MAIN DATASET: MCF7 DATASET PRODUCED USING THE DROPSEQ TECHNOLOGY

We start now opening the MCF7 DropSeq filtered normalized dataset since we are interested in analyzing it.

In [ ]:
# The dataset filtered is useful to apply statistical tools
df_MCF7_filtered = pd.read_csv("DropSeq/MCF7_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ",engine='python',index_col=0)
print("Dataframe dimensions:", np.shape(df_MCF7_filtered))
print("First column: ", df_MCF7_filtered.iloc[ : , :])

Dataframe dimensions: (3000, 21626)
First column:               "AAAAACCTATCG_Normoxia"  "AAAACAACCCTA_Normoxia"  \
"MALAT1"                           1                        3   
"MT-RNR2"                          0                        0   
"NEAT1"                            0                        0   
"H1-5"                             0                        0   
"TFF1"                             4                        1   
...                              ...                      ...   
"BRWD1-AS2"                        0                        0   
"RPS19BP1"                         0                        0   
"AUNIP"                            0                        0   
"TNK2"                             0                        0   
"SUDS3"                            0                        0   

             "AAAACACTCTCA_Normoxia"  "AAAACCAGGCAC_Normoxia"  \
"MALAT1"                           3                        6   
"MT-RNR2"                          0                        2   
"NEAT1"                            0                        0   
"H1-5"                             0                        0   
"TFF1"                             1                        1   
...                              ...                      ...   
"BRWD1-AS2"                        0                        0   
"RPS19BP1"                         0                        0   
"AUNIP"                            0                        0   
"TNK2"                             0                        0   
"SUDS3"                            0                        0   

             "AAAACCTAGCTC_Normoxia"  "AAAACCTCCGGG_Normoxia"  \
"MALAT1"                           4                        5   
"MT-RNR2"                          0                        0   
"NEAT1"                            0                        2   
"H1-5"                             0                        2   
"TFF1"                             0                        0   
...                              ...                      ...   
"BRWD1-AS2"                        0                        0   
"RPS19BP1"                         0                        0   
"AUNIP"                            0                        0   
"TNK2"                             0                        0   
"SUDS3"                            0                        0   

             "AAAACTCGTTGC_Normoxia"  "AAAAGAGCTCTC_Normoxia"  \
"MALAT1"                           1                       13   
"MT-RNR2"                          2                        1   
"NEAT1"                            0                        1   
"H1-5"                             0                        0   
"TFF1"                             0                        2   
...                              ...                      ...   
"BRWD1-AS2"                        0                        0   
"RPS19BP1"                         0                        0   
"AUNIP"                            0                        0   
"TNK2"                             0                        0   
"SUDS3"                            0                        0   

             "AAAAGCTAGGCG_Normoxia"  "AAAATCGCATTT_Normoxia"  ...  \
"MALAT1"                           3                        3  ...   
"MT-RNR2"                          7                        0  ...   
"NEAT1"                            2                        0  ...   
"H1-5"                             0                        0  ...   
"TFF1"                             0                        1  ...   
...                              ...                      ...  ...   
"BRWD1-AS2"                        0                        0  ...   
"RPS19BP1"                         0                        0  ...   
"AUNIP"                            0                        0  ...   
"TNK2"                             0                        0  ...   
"SUDS3"                            0                        0  ...   

             "TTTTACAGGATC_Hypoxia"  "TTTTACCACGTA_Hypoxia"  \
"MALAT1"                          0                       2   
"MT-RNR2"                         0                       0   
"NEAT1"                           0                       0   
"H1-5"                            0                       1   
"TFF1"                            2                       3   
...                             ...                     ...   
"BRWD1-AS2"                       0                       0   
"RPS19BP1"                        0                       0   
"AUNIP"                           0                       0   
"TNK2"                            0                       0   
"SUDS3"                           0                       0   

             "TTTTATGCTACG_Hypoxia"  "TTTTCCAGACGC_Hypoxia"  \
"MALAT1"                          1                       0   
"MT-RNR2"                         0                       0   
"NEAT1"                           0                       0   
"H1-5"                            0                       0   
"TFF1"                            8                       0   
...                             ...                     ...   
"BRWD1-AS2"                       0                       0   
"RPS19BP1"                        0                       0   
"AUNIP"                           0                       0   
"TNK2"                            0                       0   
"SUDS3"                           0                       0   

             "TTTTCGCGCTCG_Hypoxia"  "TTTTCGCGTAGA_Hypoxia"  \
"MALAT1"                          1                       0   
"MT-RNR2"                         0                       0   
"NEAT1"                           0                       0   
"H1-5"                            1                       0   
"TFF1"                            0                       3   
...                             ...                     ...   
"BRWD1-AS2"                       0                       0   
"RPS19BP1"                        0                       0   
"AUNIP"                           0                       0   
"TNK2"                            0                       0   
"SUDS3"                           0                       0   

             "TTTTCGTCCGCT_Hypoxia"  "TTTTCTCCGGCT_Hypoxia"  \
"MALAT1"                          1                       0   
"MT-RNR2"                         0                       0   
"NEAT1"                           0                       0   
"H1-5"                            0                       1   
"TFF1"                            4                       2   
...                             ...                     ...   
"BRWD1-AS2"                       0                       0   
"RPS19BP1"                        0                       0   
"AUNIP"                           0                       0   
"TNK2"                            0                       0   
"SUDS3"                           0                       1   

             "TTTTGTTCAAAG_Hypoxia"  "TTTTTTGTATGT_Hypoxia"  
"MALAT1"                          0                       4  
"MT-RNR2"                         0                       0  
"NEAT1"                           0                       0  
"H1-5"                            0                       0  
"TFF1"                            6                       0  
...                             ...                     ...  
"BRWD1-AS2"                       0                       0  
"RPS19BP1"                        0                       0  
"AUNIP"                           0                       0  
"TNK2"                            0                       0  
"SUDS3"                           0                       0  

[3000 rows x 21626 columns]
In [ ]:
df_MCF7_filtered.head()
"AAAAACCTATCG_Normoxia" "AAAACAACCCTA_Normoxia" "AAAACACTCTCA_Normoxia" "AAAACCAGGCAC_Normoxia" "AAAACCTAGCTC_Normoxia" "AAAACCTCCGGG_Normoxia" "AAAACTCGTTGC_Normoxia" "AAAAGAGCTCTC_Normoxia" "AAAAGCTAGGCG_Normoxia" "AAAATCGCATTT_Normoxia" ... "TTTTACAGGATC_Hypoxia" "TTTTACCACGTA_Hypoxia" "TTTTATGCTACG_Hypoxia" "TTTTCCAGACGC_Hypoxia" "TTTTCGCGCTCG_Hypoxia" "TTTTCGCGTAGA_Hypoxia" "TTTTCGTCCGCT_Hypoxia" "TTTTCTCCGGCT_Hypoxia" "TTTTGTTCAAAG_Hypoxia" "TTTTTTGTATGT_Hypoxia"
"MALAT1" 1 3 3 6 4 5 1 13 3 3 ... 0 2 1 0 1 0 1 0 0 4
"MT-RNR2" 0 0 0 2 0 0 2 1 7 0 ... 0 0 0 0 0 0 0 0 0 0
"NEAT1" 0 0 0 0 0 2 0 1 2 0 ... 0 0 0 0 0 0 0 0 0 0
"H1-5" 0 0 0 0 0 2 0 0 0 0 ... 0 1 0 0 1 0 0 1 0 0
"TFF1" 4 1 1 1 0 0 0 2 0 1 ... 2 3 8 0 0 3 4 2 6 0

5 rows × 21626 columns

In [ ]:
df_MCF7_filtered.shape

(3000, 21626)

The MCF7 DropSeq dataset is made of 3000 rows representing specific genes and 21626 columns representing the RNA strands. Therefore each entry tells us how much of every gene is expressed in every RNA strand. Moreover, each RNA strand has in its name written the state in which it was under (normoxia / hypoxia).

¶

3.1.1) SPARSITY OF THE DATA

Since the data is already filtered and normalized, we can directy proceed on seeing how many zeros we have and then compute the percentage of sparcity.

In [ ]:
# We proceed in the following way. We start representing the dataframe as a simple matrix to see how many zero we have 
# then we compute the percentage of the sparsity on our dataset doing 1 - number_of_non_zero_genes / total_number of genes

df_MCF7_filtered_Matrix = np.array(df_MCF7_filtered)
print("Matrix useful for the sparsity check: \n", df_MCF7_filtered_Matrix)

Matrix useful for the sparsity check: 
 [[1 3 3 ... 0 0 4]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 ...
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 1 0 0]]
In [ ]:
Sparsity_matrix_MCF7 = 1 - count_nonzero(df_MCF7_filtered_Matrix)/ df_MCF7_filtered_Matrix.size
Sparsity_matrix_MCF7

0.9752943062363205

As we can see the percentage of sparcity is extremely high. This is not something unexpected as the RNA strands are small and contain only few genes.

This leads us to skip also the outliers removal as the only genes that are expressed would in this case be detected as outliers and then we would lose all the informations.

¶

3.1.2) DATASET TRANSPOSITION AND APPLICATION OF STATISTICAL TOOLS

We transpose the dataset since having the genes in the colums and the RNA strands in the rows is more intresting under a statistical viewpoint.

In [ ]:
df_MCF7_filtered_T = df_MCF7_filtered.T
df_MCF7_filtered_T
Out[ ]:
"MALAT1" "MT-RNR2" "NEAT1" "H1-5" "TFF1" "MT-RNR1" "H4C3" "GDF15" "KRT81" "MT-CO3" ... "MROH1" "SKIDA1" "MICALL1" "RARG" "MYO1F" "BRWD1-AS2" "RPS19BP1" "AUNIP" "TNK2" "SUDS3"
"AAAAACCTATCG_Normoxia" 1 0 0 0 4 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
"AAAACAACCCTA_Normoxia" 3 0 0 0 1 0 1 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
"AAAACACTCTCA_Normoxia" 3 0 0 0 1 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
"AAAACCAGGCAC_Normoxia" 6 2 0 0 1 0 1 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
"AAAACCTAGCTC_Normoxia" 4 0 0 0 0 0 0 0 0 1 ... 0 0 0 0 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
"TTTTCGCGTAGA_Hypoxia" 0 0 0 0 3 0 7 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
"TTTTCGTCCGCT_Hypoxia" 1 0 0 0 4 0 1 0 1 0 ... 0 0 0 0 0 0 0 0 0 0
"TTTTCTCCGGCT_Hypoxia" 0 0 0 1 2 0 4 0 0 0 ... 0 0 0 0 0 0 0 0 0 1
"TTTTGTTCAAAG_Hypoxia" 0 0 0 0 6 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
"TTTTTTGTATGT_Hypoxia" 4 0 0 0 0 0 6 0 0 0 ... 0 0 0 0 0 0 0 0 0 0

21626 rows × 3000 columns

In [ ]:
df_MCF7_filtered_T_summary1 = df_MCF7_filtered_T.describe()
df_MCF7_filtered_T_summary1
Out[ ]:
"MALAT1" "MT-RNR2" "NEAT1" "H1-5" "TFF1" "MT-RNR1" "H4C3" "GDF15" "KRT81" "MT-CO3" ... "MROH1" "SKIDA1" "MICALL1" "RARG" "MYO1F" "BRWD1-AS2" "RPS19BP1" "AUNIP" "TNK2" "SUDS3"
count 21626.000000 21626.000000 21626.000000 21626.000000 21626.000000 21626.000000 21626.000000 21626.000000 21626.000000 21626.000000 ... 21626.000000 21626.000000 21626.000000 21626.000000 21626.000000 21626.000000 21626.000000 21626.000000 21626.000000 21626.000000
mean 3.634791 0.850458 0.136086 0.368862 1.799362 0.291362 1.730972 0.011884 0.026912 0.105105 ... 0.008046 0.002035 0.003930 0.016138 0.000971 0.000832 0.034172 0.002405 0.003884 0.088042
std 6.837353 1.721088 0.643060 0.953887 2.486034 0.740664 2.328460 0.189425 0.233430 0.387558 ... 0.092892 0.048995 0.065461 0.131046 0.032598 0.028839 0.200548 0.053491 0.064395 0.323733
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 2.000000 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
75% 4.000000 1.000000 0.000000 0.000000 3.000000 0.000000 2.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
max 175.000000 32.000000 23.000000 22.000000 46.000000 16.000000 44.000000 20.000000 17.000000 5.000000 ... 2.000000 3.000000 2.000000 3.000000 2.000000 1.000000 4.000000 3.000000 2.000000 3.000000

8 rows × 3000 columns

We can see that the mean of most genes is across zero. This was predictable as the sparcity analysis told us that the 97% of the entries is 0.

We then plot the 25/50/75% and we can clearly see that the non-zero genes are almost all located in the 75% quartile.

In [ ]:
#we make a histogram of the mean of the expression of the genes

sns.displot(df_MCF7_filtered_T_summary1.loc['mean'], bins=20, kde=True)
plt.xlabel('Mean of the expression of the genes')
plt.ylabel('Frequency')
plt.title('Histogram of the mean of the expression of the genes')
plt.show()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [ ]:
#we look only at the 25% quantile
sns.displot(df_MCF7_filtered_T_summary1.loc['25%'], bins=20, kde=True)
plt.xlabel('25% quantile of the expression of the genes')
plt.ylabel('Frequency')
plt.title('Histogram of the 25% quantile of the expression of the genes')
plt.show()
df_MCF7_filtered_T_summary1.loc['25%'].value_counts()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
Out[ ]:
25%
0.0    2998
1.0       2
Name: count, dtype: int64
In [ ]:
#we look only at the 50% quantile
sns.displot(df_MCF7_filtered_T_summary1.loc['50%'], bins=20, kde=True)
plt.xlabel('50% quantile of the expression of the genes')
plt.ylabel('Frequency')
plt.title('Histogram of the 50% quantile of the expression of the genes')
plt.show()
df_MCF7_filtered_T_summary1.loc['50%'].value_counts()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
Out[ ]:
50%
0.0    2985
1.0      12
2.0       2
3.0       1
Name: count, dtype: int64
In [ ]:
#we look only at the 75% quantile
sns.displot(df_MCF7_filtered_T_summary1.loc['75%'], bins=20, kde=True)
plt.xlabel('75% quantile of the expression of the genes')
plt.ylabel('Frequency')
plt.title('Histogram of the 75% quantile of the expression of the genes')
plt.show()
df_MCF7_filtered_T_summary1.loc['75%'].value_counts()

c:\Users\tommy\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
Out[ ]:
75%
0.0    2934
1.0      51
2.0      11
4.0       2
3.0       1
5.0       1
Name: count, dtype: int64

¶

3.1.3) LOOKING FOR MOST USEFUL AND EXPRESSED GENES ACROSS CELLS

Since the mean of most of the genes as seen before is near zero, we restrict our search to the genes that have at least a mean expression of 1.

Also for what regards the standard deviation we restrict our search to the genes that have a standard deviation higher 1.

We split the dataset into two parts containing normoxic RNA strands and hypoxic RNA strands.

In [ ]:
#i want to split the dataset in 2 parts, one with RNA with hypoxia in the name and the other with RNA with normoxia in the name
hypoxia_samples = df_MCF7_filtered.T[df_MCF7_filtered.T.index.str.contains('Hypoxia')]
normoxia_samples = df_MCF7_filtered.T[df_MCF7_filtered.T.index.str.contains('Normoxia')]
In [ ]:
hypoxia_samples.describe()
"MALAT1" "MT-RNR2" "NEAT1" "H1-5" "TFF1" "MT-RNR1" "H4C3" "GDF15" "KRT81" "MT-CO3" ... "MROH1" "SKIDA1" "MICALL1" "RARG" "MYO1F" "BRWD1-AS2" "RPS19BP1" "AUNIP" "TNK2" "SUDS3"
count 8921.000000 8921.000000 8921.000000 8921.000000 8921.000000 8921.000000 8921.000000 8921.000000 8921.000000 8921.000000 ... 8921.000000 8921.000000 8921.000000 8921.000000 8921.000000 8921.000000 8921.000000 8921.000000 8921.000000 8921.000000
mean 2.367672 0.097523 0.158727 0.302096 3.293241 0.014684 2.260285 0.021522 0.034189 0.010201 ... 0.006502 0.001457 0.003139 0.009976 0.001121 0.000560 0.033404 0.001794 0.003699 0.073198
std 7.290005 0.432320 0.836743 0.861408 3.071657 0.140896 2.980723 0.282102 0.306976 0.111085 ... 0.081757 0.038148 0.057908 0.100510 0.036661 0.023669 0.190010 0.044886 0.060711 0.289425
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 1.000000 0.000000 0.000000 0.000000 3.000000 0.000000 1.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
75% 2.000000 0.000000 0.000000 0.000000 5.000000 0.000000 3.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
max 175.000000 11.000000 23.000000 15.000000 46.000000 4.000000 44.000000 20.000000 17.000000 4.000000 ... 2.000000 1.000000 2.000000 2.000000 2.000000 1.000000 3.000000 2.000000 1.000000 3.000000

8 rows × 3000 columns

In [ ]:
normoxia_samples.describe()
"MALAT1" "MT-RNR2" "NEAT1" "H1-5" "TFF1" "MT-RNR1" "H4C3" "GDF15" "KRT81" "MT-CO3" ... "MROH1" "SKIDA1" "MICALL1" "RARG" "MYO1F" "BRWD1-AS2" "RPS19BP1" "AUNIP" "TNK2" "SUDS3"
count 12705.000000 12705.000000 12705.000000 12705.000000 12705.000000 12705.000000 12705.000000 12705.000000 12705.000000 12705.000000 ... 12705.000000 12705.000000 12705.000000 12705.000000 12705.000000 12705.000000 12705.000000 12705.000000 12705.000000 12705.000000
mean 4.524518 1.379142 0.120189 0.415742 0.750413 0.485636 1.359307 0.005116 0.021802 0.171743 ... 0.009130 0.002440 0.004486 0.020464 0.000866 0.001023 0.034711 0.002834 0.004014 0.098465
std 6.351683 2.057514 0.460112 1.011196 1.108189 0.910149 1.629650 0.071346 0.162862 0.486050 ... 0.099961 0.055353 0.070278 0.148640 0.029413 0.031973 0.207634 0.058783 0.066863 0.345425
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 2.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
75% 6.000000 2.000000 0.000000 0.000000 1.000000 1.000000 2.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
max 131.000000 32.000000 11.000000 22.000000 9.000000 16.000000 39.000000 1.000000 3.000000 5.000000 ... 2.000000 3.000000 2.000000 3.000000 1.000000 1.000000 4.000000 3.000000 2.000000 3.000000

8 rows × 3000 columns

In [ ]:
#we look at how many genes have a mean of expression greater than 1 and we print their names
#for hypoxia samples    
hypoxia_samples_summary = hypoxia_samples.describe()

hypo_genes = hypoxia_samples_summary.loc['mean'][hypoxia_samples_summary.loc['mean'] > 1]
hypo_genes_sorted = sorted(hypo_genes.items(), key=lambda x: x[1], reverse=True)
print(hypo_genes_sorted)
print(f"{len(hypo_genes_sorted)} genes have a mean of expression greater than 1 in hypoxia samples")

## Now we apply the same with the standard deviation
hypo_genes_std = hypoxia_samples_summary.loc['std'][hypoxia_samples_summary.loc['std'] > 1]
hypo_genes_std_sorted = sorted(hypo_genes_std.items(), key=lambda x: x[1], reverse=True)
print(hypo_genes_std_sorted)
print(f"{len(hypo_genes_std_sorted)} genes have a standard deviation greater than 1 in hypoxia samples")

[('"RPS19"', 4.0482008743414415), ('"BCYRN1"', 3.5211299181706086), ('"TFF1"', 3.2932406680865376), ('"RPL13"', 2.500616522811344), ('"MALAT1"', 2.367671785674252), ('"RPL35"', 2.3223853828046184), ('"H4C3"', 2.2602847214437842), ('"RPS14"', 2.1470687142697007), ('"RPLP1"', 1.8000224190113217), ('"RPL12"', 1.6857975563277658), ('"RPS27"', 1.6446586705526287), ('"RPS12"', 1.5113776482457124), ('"HSPB1"', 1.467548481111983), ('"KRT8"', 1.4604864925456786), ('"RPL35A"', 1.340881067144939), ('"RPL34"', 1.3203676717856743), ('"TMSB10"', 1.2384261854052236), ('"RPLP2"', 1.184396368120166), ('"RPS5"', 1.1486380450622127), ('"RPL15"', 1.1452751933639727), ('"RPL39"', 1.1373164443448045), ('"RPL36"', 1.1111982961551397), ('"SERF2"', 1.1023427866831073), ('"RPS15A"', 1.0842954825692186), ('"RPS16"', 1.06064342562493), ('"KRT19"', 1.0517879161528976), ('"KRT18"', 1.0284721443784328), ('"RPS28"', 1.0112095056608004)]
28 genes have a mean of expression greater than 1 in hypoxia samples
[('"MALAT1"', 7.290005109760247), ('"BCYRN1"', 3.7218640607052977), ('"TFF1"', 3.071656963858318), ('"H4C3"', 2.9807231733949235), ('"RPS19"', 2.926860177044239), ('"RPL13"', 1.9134362829982945), ('"RPL35"', 1.9125154133162319), ('"RPS14"', 1.680441627534648), ('"RPL12"', 1.5961799461360446), ('"RPS12"', 1.5692210218211629), ('"RPLP1"', 1.5016896311743007), ('"RPS27"', 1.4918150374677128), ('"KRT8"', 1.3797998769815072), ('"TMSB10"', 1.3764461414196563), ('"HSPB1"', 1.3746934140582237), ('"RPS5"', 1.3612554361175306), ('"RPL35A"', 1.331170827023664), ('"RPLP2"', 1.2815379272648442), ('"RPL39"', 1.2767666629509271), ('"RPL34"', 1.257372393388114), ('"RPL36"', 1.2223484752177272), ('"RPS16"', 1.2191572434117108), ('"RPS28"', 1.2024302301824568), ('"RPS15A"', 1.2003647519785692), ('"KRT18"', 1.163956454202145), ('"ACTB"', 1.153635928043784), ('"DSCAM-AS1"', 1.1517097320184762), ('"RPL15"', 1.1476102753562882), ('"SERF2"', 1.1372987939015213), ('"LGALS1"', 1.1367763145299683), ('"S100A10"', 1.136580121061228), ('"FTL"', 1.1301379022905567), ('"KRT19"', 1.0725934572437787), ('"UQCRQ"', 1.0659433638098892), ('"RPS15"', 1.0621915646477988), ('"ELOB"', 1.0372161788541507), ('"RPL37A"', 1.0214144440509616)]
37 genes have a standard deviation greater than 1 in hypoxia samples

We can see that in hypoxic RNA strands most of the genes that have a mean higher than 1 have also the standard deviation higher than 1.

In [ ]:
normoxia_samples_summary = normoxia_samples.describe()

normo_genes = normoxia_samples_summary.loc['mean'][normoxia_samples_summary.loc['mean'] > 1]
normo_genes_sorted = sorted(normo_genes.items(), key=lambda x: x[1], reverse=True)
print(normo_genes_sorted)
print(f" {len(normo_genes_sorted)} genes have a mean of expression greater than 92.814 in normoxia samples")

normo_genes_std = normoxia_samples_summary.loc['std'][normoxia_samples_summary.loc['std'] > 1]
normo_genes_std_sorted = sorted(normo_genes_std.items(), key=lambda x: x[1], reverse=True)
print(normo_genes_std_sorted)
print(f"{len(normo_genes_std_sorted)} genes have a standard deviation greater than 75.526 in normoxia samples")

[('"MALAT1"', 4.524517906336088), ('"BCYRN1"', 3.188902007083825), ('"RPS19"', 1.6436835891381345), ('"ACTB"', 1.486107831562377), ('"MT-RNR2"', 1.379142070051161), ('"H4C3"', 1.3593073593073592), ('"SET"', 1.0544667453758363)]
 7 genes have a mean of expression greater than 92.814 in normoxia samples
[('"MALAT1"', 6.35168300075444), ('"BCYRN1"', 2.6682761014979333), ('"MT-RNR2"', 2.0575143475434845), ('"RPS19"', 1.6724908743586877), ('"H4C3"', 1.6296499520005536), ('"ACTB"', 1.4974211151986756), ('"KRT8"', 1.3604395304461172), ('"SET"', 1.2023771130527212), ('"RPL35"', 1.2000112687491644), ('"RPL13"', 1.1818455470828557), ('"TFF1"', 1.1081888465981982), ('"RPLP1"', 1.085824228127824), ('"RPS27"', 1.0852468414036733), ('"HSPB1"', 1.0825363004278141), ('"RPS14"', 1.0771059085798058), ('"RPS12"', 1.059035505175214), ('"H1-5"', 1.0111956580136596)]
17 genes have a standard deviation greater than 75.526 in normoxia samples

Also for normoxic RNA strands most of the genes that have a mean higher than 1 have also the standard deviation higher than 1.

In [ ]:
# Transpose the DataFrames
hypoxia_summary_t = hypoxia_samples_summary.transpose()
normoxia_summary_t = normoxia_samples_summary.transpose()

# Calculate the difference in means
diff_mean = abs(hypoxia_summary_t['mean'] - normoxia_summary_t['mean'])

# Sort genes by difference in mean in descending order
sorted_genes = diff_mean.sort_values(ascending=False)

# Get the list of genes
genes_list = sorted_genes.index.tolist()
genes_list

# Create a DataFrame from the sorted series
sorted_genes_df = sorted_genes.reset_index()

# Rename the columns
sorted_genes_df.columns = ['Gene', 'Difference']

# Display the DataFrame
sorted_genes_df[:50]
Gene Difference
0 "TFF1" 2.542827
1 "RPS19" 2.404517
2 "MALAT1" 2.156846
3 "RPL13" 1.569094
4 "RPL35" 1.386297
5 "RPS14" 1.365172
6 "MT-RNR2" 1.281619
7 "RPL12" 1.054707
8 "TMSB10" 1.041653
9 "RPLP1" 0.979401
10 "H4C3" 0.900977
11 "RPL34" 0.837802
12 "KRT19" 0.836440
13 "RPS27" 0.818449
14 "RPS5" 0.813731
15 "RPL35A" 0.803219
16 "LGALS1" 0.801535
17 "RPS12" 0.773322
18 "RPL15" 0.747951
19 "RPL39" 0.731020
20 "RPL36" 0.685697
21 "RPLP2" 0.664837
22 "HSPB1" 0.650311
23 "RPS15A" 0.645334
24 "RPS16" 0.632702
25 "S100A10" 0.622019
26 "SERF2" 0.610647
27 "GAPDH" 0.588966
28 "TPI1" 0.586619
29 "RPS15" 0.534018
30 "RPL30" 0.528922
31 "UQCRQ" 0.525043
32 "ELOB" 0.512870
33 "SET" 0.507891
34 "FTL" 0.506052
35 "ACTB" 0.499447
36 "ENO1" 0.483760
37 "PGK1" 0.481790
38 "KRT18" 0.476327
39 "MT-RNR1" 0.470951
40 "TMEM258" 0.470342
41 "RPL37A" 0.469123
42 "KRT8" 0.466705
43 "ROMO1" 0.451809
44 "RPS28" 0.449777
45 "HES1" 0.428981
46 "COX7A2" 0.415697
47 "SNRPD2" 0.409989
48 "PKM" 0.404241
49 "UBA52" 0.381792
In [ ]:
#we look at how many genes have a mean of expression greater than 1 and we print their names
#for hypoxia samples    
hypoxia_samples_summary = hypoxia_samples.describe()

hypo_genes = hypoxia_samples_summary.loc['mean'][hypoxia_samples_summary.loc['mean'] > 1]
hypo_genes

"MALAT1"    2.367672
"TFF1"      3.293241
"H4C3"      2.260285
"TMSB10"    1.238426
"BCYRN1"    3.521130
"RPS5"      1.148638
"RPL12"     1.685798
"RPS12"     1.511378
"RPL39"     1.137316
"RPS16"     1.060643
"RPL35"     2.322385
"RPS19"     4.048201
"RPLP2"     1.184396
"RPL36"     1.111198
"RPL13"     2.500617
"RPS28"     1.011210
"RPL35A"    1.340881
"KRT19"     1.051788
"RPS14"     2.147069
"RPL34"     1.320368
"RPS15A"    1.084295
"KRT8"      1.460486
"RPL15"     1.145275
"RPS27"     1.644659
"KRT18"     1.028472
"HSPB1"     1.467548
"RPLP1"     1.800022
"SERF2"     1.102343
Name: mean, dtype: float64
In [ ]:
#we do it also for normoxia samples
normoxia_samples_summary = normoxia_samples.describe()
normo_genes = normoxia_samples_summary.loc['mean'][normoxia_samples_summary.loc['mean'] > 1]
normo_genes 

"MALAT1"     4.524518
"MT-RNR2"    1.379142
"H4C3"       1.359307
"BCYRN1"     3.188902
"RPS19"      1.643684
"SET"        1.054467
"ACTB"       1.486108
Name: mean, dtype: float64

We inspect the functions of the genes that have the highest difference in mean between the two samples:

The gene that has the highest difference is TFF1, we can see it as it's upregulated in hypoxia samples, this can highlight a relation as, while TFF1 is primarily known for its role in mucosal protection and repair in the gastrointestinal tract, it's plausible that its expression could be influenced by hypoxia in certain cellular contexts. For example, in tissues experiencing hypoxia due to compromised blood flow or ischemia, there may be alterations in the expression of genes involved in mucosal protection and repair, including TFF1.

We see that the gene MALAT1 is under-regulated in hypoxia samples, This can be a good indicator of hypoxia since studies have shown that MALAT1 expression can be modulated by HIFs under hypoxic conditions.

Moreover, the gene RPL13 is upregulated in hypoxia samples, also this feature can highlight some link as ribosomal proteins like RPL13 are primarily known for their role in protein synthesis and ribosome function, it's plausible that their expression could be influenced by hypoxia in certain cellular contexts, in fact some studies have indicated that the expression of ribosomal proteins, including RPL13, can be influenced by various cellular stressors and environmental conditions, including hypoxia.

In contrast, MT-RNR2 is downregulated in hypoxia, which is consistent with the known downregulation of mitochondrial activity in hypoxic conditions. in fact we have a mean of 0.097 in hypoxia and 1.38 in normoxia

Moreover the gene RPS19 is higly upregulated in the hypoxia samples, but in this case there is no evidence that it is correlated to the hipoxic condition as RPS19 expression is important for normal cellular function and its dysregulation can lead to diseases such as Diamond-Blackfan anemia, it's unlikely to be upregulated in hypoxic cells and may even be downregulated as part of the cellular response to low oxygen levels.

In [ ]:
## Now with the std
# Transpose the DataFrames
hypoxia_summary_t = hypoxia_samples_summary.transpose()
normoxia_summary_t = normoxia_samples_summary.transpose()

# Calculate the difference in means
diff_std = abs(hypoxia_summary_t['std'] - normoxia_summary_t['std'])

# Sort genes by difference in mean in descending order
sorted_genes_std = diff_std.sort_values(ascending=False)

# Get the list of genes
genes_list = sorted_genes_std.index.tolist()
genes_list

# Create a DataFrame from the sorted series
sorted_genes_df = sorted_genes_std.reset_index()

# Rename the columns
sorted_genes_df.columns = ['Gene', 'Difference']

# Display the DataFrame
sorted_genes_df[:50]
Gene Difference
0 "TFF1" 1.963468
1 "MT-RNR2" 1.625194
2 "H4C3" 1.351073
3 "RPS19" 1.254369
4 "BCYRN1" 1.053588
5 "MALAT1" 0.938322
6 "TMSB10" 0.849760
7 "MT-RNR1" 0.769253
8 "RPL13" 0.731591
9 "RPL35" 0.712504
10 "LGALS1" 0.702786
11 "RPS5" 0.652149
12 "RPL12" 0.621375
13 "RPS14" 0.603336
14 "KRT19" 0.516626
15 "RPL39" 0.516618
16 "RPS12" 0.510186
17 "HES1" 0.507390
18 "TPI1" 0.492272
19 "GAPDH" 0.479824
20 "RPL35A" 0.469144
21 "PGK1" 0.456893
22 "S100A10" 0.454374
23 "MT-ND4" 0.442999
24 "RPL36" 0.440201
25 "RPLP2" 0.426425
26 "TFF3" 0.425001
27 "RPS16" 0.420057
28 "RPS15A" 0.419366
29 "RPL34" 0.419134
30 "RPLP1" 0.415865
31 "RPL15" 0.411792
32 "RPS27" 0.406568
33 "SET" 0.386002
34 "RPL30" 0.384034
35 "NEAT1" 0.376632
36 "MT-CO3" 0.374965
37 "SOX4" 0.369440
38 "MT-ND3" 0.362084
39 "TOB1" 0.360845
40 "MT-CYB" 0.360220
41 "ROMO1" 0.349800
42 "RPS15" 0.344840
43 "ACTB" 0.343785
44 "S100A11" 0.340564
45 "DSCAM-AS1" 0.338762
46 "PARD6B" 0.338344
47 "TMEM258" 0.336613
48 "ATP1A1" 0.331619
49 "SNRPD2" 0.329961

Even studying the std,the results are confirmed, genes in the first position are the same as before.

¶

3.1.4) UNSUPERVISD LEARNING APPLIED TO MCF7

We firstly decided to do a PCA of two components and then to perform a kmeans clustering.

The result was bad because, as can be seen from the first picture, the datapoints were not linearly separable.

The performance of the kmins in fact is just a division in two parts of the graph.

In [ ]:
def extract_condition(name):
    match = re.search(r'Normoxia|Hypoxia', name)
    if match:
        return match.group()
    return 'unknown'


pca = PCA(n_components=2)
principalComponents = pca.fit_transform(df_MCF7_filtered_T)

k = 2
kmeans = KMeans(n_clusters=k, init='k-means++',random_state=0, n_init=10)
clusters = kmeans.fit_predict(principalComponents)

plt.figure(figsize=(12, 6))

# Assign colors to conditions manually
color_map = {'Hypoxia': 'red', 'Normoxia': 'blue', 'Unknown': 'green'}
conditions = df_MCF7_filtered_T.index.to_series().apply(extract_condition)
colors = conditions.map(color_map)

plt.scatter(principalComponents[:, 0], principalComponents[:, 1], c=colors)
plt.title('PCA of Cells Colored by Condition')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')

# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i], 
            label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')

plt.show()
No description has been provided for this image
In [ ]:
# Visualization of PCA with K-means clusters, with a proper legend
plt.figure(figsize=(12, 6))

# Assign colors based on the K-means cluster result
# Here we assign 'green' to cluster 1 and 'yellow' to cluster 2
cluster_colors = ['green' if label == 0 else 'yellow' for label in clusters]

# Scatter plot with assigned colors
scatter = plt.scatter(principalComponents[:, 0], principalComponents[:, 1], c=cluster_colors)

# Create a legend for the colors
plt.title('PCA of Cells Colored by K-means Clusters')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')

# Create a legend for the colors
legend_labels = {0: 'cluster 1', 1: 'cluster 2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
                      markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='Division')
plt.show()
No description has been provided for this image

Then we tried to apply a PCA of 3 components and then the kmins clustering.

Also in this case the datapoints were not linearly separable and thus the performance of the kmins was terrible.

In [ ]:
#now i want to do a pca with 3 components
pca = PCA(n_components=3)
principalComponents3 = pca.fit_transform(df_MCF7_filtered_T)
clusters3 = kmeans.fit_predict(principalComponents3)
In [ ]:
# Visualization of PCA in 3 dimensions, with a proper legend
pca = PCA(n_components=3)
principalComponents3 = pca.fit_transform(df_MCF7_filtered_T)
clusters3 = kmeans.fit_predict(principalComponents3)

fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(111, projection='3d')

# Assign colors to conditions manually
colors = conditions.map(color_map)

# Scatter plot with assigned colors
scatter = ax.scatter(principalComponents3[:, 0], principalComponents3[:, 1], principalComponents3[:, 2], c=colors)

# Create a legend for the colors
ax.set_title('PCA of Cells Colored by Condition')
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')

# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i], 
            label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')

plt.show()
No description has been provided for this image
In [ ]:
# Visualization of PCA with K-means clusters, with a proper legend
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(111, projection='3d')

# Assign colors based on the K-means cluster result
# Here we assign 'green' to cluster 1 and 'yellow' to cluster 0
cluster_colors3 = ['green' if label == 0 else 'yellow' for label in clusters3]

# Scatter plot with assigned colors
scatter = ax.scatter(principalComponents3[:, 0], principalComponents3[:, 1], principalComponents3[:, 2], c=cluster_colors3)

# Create a legend for the colors
ax.set_title('PCA of Cells Colored by K-means Clusters')
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')

# Create a legend for the colors
legend_labels = {0: 'cluster 1', 1: 'cluster 2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
                      markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='Division')

plt.show()
No description has been provided for this image

As a last chance, we then tried to apply the t-Distributed Stochastic Neighbor Embedding (t-SNE) to our data.

We did it as the t-SNE is a non-linear technique that is well-suited for capturing non-linear relationships in the data. It uses a non-linear optimization algorithm to map high-dimensional data points to a lower-dimensional space, making it more suitable for visualizing complex data with non-linear structures.

But also in this case the result was not good, both in the 2-dimensional and 3-dimensional case the datapoints were concentrated and not separable.

Thus the performance of k-mins was bad.

In [ ]:
from sklearn.manifold import TSNE

# Dimensionality reduction with t-SNE
tsne3 = TSNE(n_components=3)
embedded_data3 = tsne3.fit_transform(df_MCF7_filtered_T)

# Apply k-means clustering to the t-SNE result
kmeans = KMeans(n_clusters=k, init='k-means++',random_state=0, n_init=10)
clustersT3 = kmeans.fit_predict(embedded_data3)

# Visualization of t-SNE in 3 dimensions, with a proper legend
fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(111, projection='3d')

# Assign colors to conditions manually
colors = conditions.map(color_map)

# Scatter plot with assigned colors
scatter = ax.scatter(embedded_data3[:, 0], embedded_data3[:, 1], embedded_data3[:, 2], c=colors)

# Create a legend for the colors
ax.set_title('t-SNE of Cells Colored by Condition')
ax.set_xlabel('t-SNE Dimension 1')
ax.set_ylabel('t-SNE Dimension 2')
ax.set_zlabel('t-SNE Dimension 3')

# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i], 
            label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')

plt.show()
No description has been provided for this image
In [ ]:
#plot the clusters
fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(111, projection='3d')

# Assign colors based on the Spectral Clustering result
cluster_colorsT3 = ['green' if label == 0 else 'yellow' for label in clustersT3]

# Scatter plot with assigned colors
scatter = ax.scatter(embedded_data3[:, 0], embedded_data3[:, 1], embedded_data3[:, 2], c=cluster_colorsT3)

# Create a legend for the colors
ax.set_title('t-SNE of Cells Colored by Kmins Clustering')
ax.set_xlabel('t-SNE Dimension 1')
ax.set_ylabel('t-SNE Dimension 2')
ax.set_zlabel('t-SNE Dimension 3')

# Assuming that after any potential inversion, cluster 0 corresponds to Normoxia and cluster 1 to Hypoxia
legend_labels = {0: 'cluster 1', 1: 'cluster 2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
                      markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='Division')

plt.show()
No description has been provided for this image
In [ ]:
from sklearn.manifold import TSNE

# Dimensionality reduction with t-SNE
tsne2 = TSNE(n_components=2)
embedded_data2 = tsne2.fit_transform(df_MCF7_filtered_T)

plt.figure(figsize=(12, 6))

#calculate the match rate with a k-means clustering
kmeansT2 = KMeans(n_clusters=2, init='k-means++',random_state=0, n_init=10)
clustersT2 = kmeansT2.fit_predict(embedded_data2)

# Assign colors to conditions manually
color_map = {'Hypoxia': 'red', 'Normoxia': 'blue', 'Unknown': 'green'}
colors = conditions.map(color_map)

plt.scatter(embedded_data2[:, 0], embedded_data2[:, 1], c=colors)
plt.title('t-SNE of Cells Colored by Condition')
plt.xlabel('t-SNE Component 1')
plt.ylabel('t-SNE Component 2')

# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i], 
            label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')

plt.show()
No description has been provided for this image
In [ ]:
#plot the clusters

plt.figure(figsize=(12, 6))

# Assign colors based on the K-means cluster result
cluster_colorsT2 = ['green' if label == 0 else 'yellow' for label in clustersT2]

# Scatter plot with assigned colors
scatter = plt.scatter(embedded_data2[:, 0], embedded_data2[:, 1], c=cluster_colorsT2)

# Create a legend for the colors
plt.title('t-SNE of Cells Colored by K-means Clusters')
plt.xlabel('t-SNE Component 1')
plt.ylabel('t-SNE Component 2')

# Assuming that after any potential inversion, cluster 0 corresponds to Normoxia and cluster 1 to Hypoxia
legend_labels = {0: 'cluster 1', 1: 'cluster 2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
                      markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='Division')

plt.show()
No description has been provided for this image

For the sake of curiosity, we finally tried to perform a "manual" PCA selecting 3 of the genes that where found as importantin the previous analysis.

But also the result we found was not separable and thus the performance of kmins was also in this case bad.

In [ ]:
selected_genes = ['"TFF1"', '"MT-RNR2"', '"MALAT1"']
selected_genes_df = df_MCF7_filtered.loc[selected_genes]

# Transpose the DataFrame
selected_genes_df = selected_genes_df.T

# Apply PCA to the selected genes
pca = PCA(n_components=3)
selected_genes_transformed = pca.fit_transform(selected_genes_df)

# Assign colors to conditions manually
conditions = selected_genes_df.index.to_series().apply(extract_condition)
color_map = {'Hypoxia': 'red', 'Normoxia': 'blue'}
colors = conditions.map(color_map)

# Create a 3D scatter plot
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(selected_genes_transformed[:, 0], selected_genes_transformed[:, 1], selected_genes_transformed[:, 2], c=colors)
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
plt.title('PCA of Selected Genes (MT_RNR2, TFF1, MALAT1) Colored by Condition')
plt.show()
No description has been provided for this image
In [ ]:
kmeans = KMeans(n_clusters=2, random_state=42)
clusters1 = kmeans.fit_predict(selected_genes_transformed)

# Assign colors to conditions manually
conditions = selected_genes_df.index.to_series().apply(extract_condition)
color_map = {'Hypoxia': 'green', 'Normoxia': 'yellow'}
colors = conditions.map(color_map)
cluster_colorsH = ['yellow' if label == 0 else 'green' for label in clusters1]

# Create a 3D scatter plot
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(selected_genes_transformed[:, 0], selected_genes_transformed[:, 1], selected_genes_transformed[:, 2], c=cluster_colorsH)
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
plt.title('K-means Clustering of Selected Genes (GAPDH, ALDOA, PGK1)')
plt.show()

/Users/teodorotoscano/opt/anaconda3/lib/python3.8/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
No description has been provided for this image

¶

3.1.5) More unsupervised learning

In [ ]:
X_MCF7_DROP = df_trans_MCF7_DROP.drop('TARGET', axis=1)
y_train_MCF7_DROP=df_trans_MCF7_DROP['TARGET']

scaler_MCF7_DROP = StandardScaler()
X_scaled_MCF7_DROP = scaler_MCF7_DROP.fit_transform(X_MCF7_DROP)
In [ ]:
pca2_MCF7_DROP = PCA(n_components=2)
X_pca2_MCF7_DROP = pca2_MCF7_DROP.fit_transform(X_scaled_MCF7_DROP)
In [ ]:
plt.figure(figsize=(12, 6))

plt.scatter(X_pca2_MCF7_DROP[:,0][y_train_MCF7_DROP==1], X_pca2_MCF7_DROP[:,1][y_train_MCF7_DROP==1], c='r')
plt.scatter(X_pca2_MCF7_DROP[:,0][y_train_MCF7_DROP==0], X_pca2_MCF7_DROP[:,1][y_train_MCF7_DROP==0], c='b')

plt.title("Clusterization PCA MCF7_DROP")

plt.show()
No description has been provided for this image
In [ ]:
'''% of EXPLAINED VARIANCE'''
pca_MCF7_DROP = PCA().fit(X_scaled_MCF7_DROP)
In [ ]:
explained_variance_ratio_MCF7_DROP = pca_MCF7_DROP.explained_variance_ratio_
cumulative_variance_MCF7_DROP = explained_variance_ratio_MCF7_DROP.cumsum()

plt.plot(range(1, len(cumulative_variance_MCF7_DROP) + 1), cumulative_variance_MCF7_DROP, marker='o')
plt.xlabel("Number of components")
plt.ylabel("Cumulative proportion of Variance Explained")
plt.title("Analysis of explained variance")
plt.show()
No description has been provided for this image
In [ ]:
tsne_model_MCF7_DROP = TSNE(n_components=2) #, perplexity=10
X_tsne_MCF7_DROP = tsne_model_MCF7_DROP.fit_transform(X_scaled_MCF7_DROP)
In [ ]:
# Visualization of results
plt.figure(figsize=(12, 6))
# plt.scatter(X_train_pca[:,0], X_train_pca[:,1], cmap='viridis')


plt.scatter(X_tsne_MCF7_DROP[:,0][y_train_MCF7_DROP==1], X_tsne_MCF7_DROP[:,1][y_train_MCF7_DROP==1], c='r')
plt.scatter(X_tsne_MCF7_DROP[:,0][y_train_MCF7_DROP==0], X_tsne_MCF7_DROP[:,1][y_train_MCF7_DROP==0], c='b')

plt.title("Clusterization TSNE ")

plt.show()
No description has been provided for this image

¶

3.2) HCC1806

¶

3.2.0) OPENING THE MAIN DATASET: HCC1806 DATASET PRODUCED USING THE DROPSEQ TECHNOLOGY

We start now opening the HCC1806 DropSeq filtered normalized dataset since we are interested in analyzing it.

In [ ]:
# The dataset filtered is useful to apply statistical tools
df_HCC1806_filtered = pd.read_csv("DropSeq/HCC1806_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ",engine='python',index_col=0)
print("Dataframe dimensions:", np.shape(df_HCC1806_filtered))
print("First column: ", df_HCC1806_filtered.iloc[ : , :])

Dataframe dimensions: (3000, 14682)
First column:             "AAAAAACCCGGC_Normoxia"  "AAAACCGGATGC_Normoxia"  \
"H1-5"                           2                        2   
"MALAT1"                         3                        3   
"MT-RNR2"                        0                        0   
"ARVCF"                          0                        0   
"BCYRN1"                         0                        1   
...                            ...                      ...   
"SCCPDH"                         0                        0   
"NTAN1"                          0                        0   
"CLIP2"                          0                        0   
"DUSP23"                         0                        0   
"ZNF682"                         0                        0   

           "AAAACGAGCTAG_Normoxia"  "AAAACTTCCCCG_Normoxia"  \
"H1-5"                           5                        1   
"MALAT1"                         2                        3   
"MT-RNR2"                        0                        0   
"ARVCF"                          0                        0   
"BCYRN1"                         1                        0   
...                            ...                      ...   
"SCCPDH"                         0                        0   
"NTAN1"                          0                        0   
"CLIP2"                          0                        0   
"DUSP23"                         0                        0   
"ZNF682"                         0                        0   

           "AAAAGCCTACCC_Normoxia"  "AAACACAAATCT_Normoxia"  \
"H1-5"                           0                        0   
"MALAT1"                        12                        3   
"MT-RNR2"                        0                        0   
"ARVCF"                          0                        0   
"BCYRN1"                         0                        1   
...                            ...                      ...   
"SCCPDH"                         0                        0   
"NTAN1"                          0                        0   
"CLIP2"                          0                        0   
"DUSP23"                         0                        0   
"ZNF682"                         0                        0   

           "AAACCAAGCCCA_Normoxia"  "AAACCATGCACT_Normoxia"  \
"H1-5"                           0                        0   
"MALAT1"                         1                        2   
"MT-RNR2"                        0                        0   
"ARVCF"                          0                        0   
"BCYRN1"                         1                        2   
...                            ...                      ...   
"SCCPDH"                         0                        0   
"NTAN1"                          0                        0   
"CLIP2"                          0                        0   
"DUSP23"                         0                        0   
"ZNF682"                         0                        0   

           "AAACCTCCGGCT_Normoxia"  "AAACGCCGGTCC_Normoxia"  ...  \
"H1-5"                           1                        0  ...   
"MALAT1"                         0                        0  ...   
"MT-RNR2"                        0                        1  ...   
"ARVCF"                          0                        0  ...   
"BCYRN1"                         0                        3  ...   
...                            ...                      ...  ...   
"SCCPDH"                         0                        0  ...   
"NTAN1"                          0                        0  ...   
"CLIP2"                          0                        0  ...   
"DUSP23"                         0                        0  ...   
"ZNF682"                         0                        0  ...   

           "TTTTCTGATGGT_Hypoxia"  "TTTTGATTCAGA_Hypoxia"  \
"H1-5"                          0                       1   
"MALAT1"                        3                       1   
"MT-RNR2"                       1                       2   
"ARVCF"                         0                       0   
"BCYRN1"                        1                       1   
...                           ...                     ...   
"SCCPDH"                        0                       0   
"NTAN1"                         0                       0   
"CLIP2"                         0                       0   
"DUSP23"                        0                       0   
"ZNF682"                        0                       0   

           "TTTTGCAACTGA_Hypoxia"  "TTTTGCCGGGCC_Hypoxia"  \
"H1-5"                          0                       2   
"MALAT1"                        1                       1   
"MT-RNR2"                       2                       2   
"ARVCF"                         0                       0   
"BCYRN1"                        0                       1   
...                           ...                     ...   
"SCCPDH"                        0                       0   
"NTAN1"                         0                       0   
"CLIP2"                         0                       0   
"DUSP23"                        0                       0   
"ZNF682"                        0                       0   

           "TTTTGTTAGCCT_Hypoxia"  "TTTTTACCAATC_Hypoxia"  \
"H1-5"                          1                       0   
"MALAT1"                        4                       0   
"MT-RNR2"                       0                       0   
"ARVCF"                         0                       0   
"BCYRN1"                        1                       0   
...                           ...                     ...   
"SCCPDH"                        0                       0   
"NTAN1"                         1                       0   
"CLIP2"                         0                       0   
"DUSP23"                        0                       0   
"ZNF682"                        0                       0   

           "TTTTTCCGTGCA_Hypoxia"  "TTTTTGCCTGGG_Hypoxia"  \
"H1-5"                          0                       0   
"MALAT1"                        4                       1   
"MT-RNR2"                       1                       0   
"ARVCF"                         0                       0   
"BCYRN1"                        0                       1   
...                           ...                     ...   
"SCCPDH"                        0                       0   
"NTAN1"                         0                       0   
"CLIP2"                         0                       0   
"DUSP23"                        0                       0   
"ZNF682"                        0                       0   

           "TTTTTGTAACAG_Hypoxia"  "TTTTTTTGAATC_Hypoxia"  
"H1-5"                          3                       1  
"MALAT1"                        3                       6  
"MT-RNR2"                       1                       0  
"ARVCF"                         0                       0  
"BCYRN1"                        0                       0  
...                           ...                     ...  
"SCCPDH"                        0                       0  
"NTAN1"                         0                       0  
"CLIP2"                         0                       0  
"DUSP23"                        0                       0  
"ZNF682"                        0                       0  

[3000 rows x 14682 columns]
In [ ]:
df_HCC1806_filtered.head()
"AAAAAACCCGGC_Normoxia" "AAAACCGGATGC_Normoxia" "AAAACGAGCTAG_Normoxia" "AAAACTTCCCCG_Normoxia" "AAAAGCCTACCC_Normoxia" "AAACACAAATCT_Normoxia" "AAACCAAGCCCA_Normoxia" "AAACCATGCACT_Normoxia" "AAACCTCCGGCT_Normoxia" "AAACGCCGGTCC_Normoxia" ... "TTTTCTGATGGT_Hypoxia" "TTTTGATTCAGA_Hypoxia" "TTTTGCAACTGA_Hypoxia" "TTTTGCCGGGCC_Hypoxia" "TTTTGTTAGCCT_Hypoxia" "TTTTTACCAATC_Hypoxia" "TTTTTCCGTGCA_Hypoxia" "TTTTTGCCTGGG_Hypoxia" "TTTTTGTAACAG_Hypoxia" "TTTTTTTGAATC_Hypoxia"
"H1-5" 2 2 5 1 0 0 0 0 1 0 ... 0 1 0 2 1 0 0 0 3 1
"MALAT1" 3 3 2 3 12 3 1 2 0 0 ... 3 1 1 1 4 0 4 1 3 6
"MT-RNR2" 0 0 0 0 0 0 0 0 0 1 ... 1 2 2 2 0 0 1 0 1 0
"ARVCF" 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
"BCYRN1" 0 1 1 0 0 1 1 2 0 3 ... 1 1 0 1 1 0 0 1 0 0

5 rows × 14682 columns

In [ ]:
df_HCC1806_filtered.shape

(3000, 14682)

The MCF7 DropSeq dataset is made of 3000 rows representing specific genes and 14682 columns representing the RNA strands. Therefore each entry tells us how much of every gene is expressed in every RNA strand. Moreover, each RNA strand has in its name written the state in which it was under (normoxia / hypoxia).

¶

3.2.1) SPARSITY OF THE DATA

Since the data is already filtered and normalized, we can directy proceed on seeing how many zeros we have and then compute the percentage of sparcity.

In [ ]:
# We proceed in the following way. We start representing the dataframe as a simple matrix to see how many zero we have 
# then we compute the percentage of the sparsity on our dataset doing 1 - number_of_non_zero_genes / total_number of genes

df_HCC1806_filtered_Matrix = np.array(df_HCC1806_filtered)
print("Matrix useful for the sparsity check: \n", df_HCC1806_filtered_Matrix)

Matrix useful for the sparsity check: 
 [[2 2 5 ... 0 3 1]
 [3 3 2 ... 1 3 6]
 [0 0 0 ... 0 1 0]
 ...
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]]
In [ ]:
Sparsity_matrix_HCC1806 = 1 - count_nonzero(df_HCC1806_filtered_Matrix)/ df_HCC1806_filtered_Matrix.size
Sparsity_matrix_HCC1806

0.9763756073196204

As we can see also for this dataset the percentage of sparcity is extremely high. This is not something unexpected as the RNA strands are small and contain only few genes.

This leads us also in this case to skip the outliers removal as the only genes that are expressed would in this case be detected as outliers and then we would lose all the informations.

¶

3.2.2) DATASET TRANSPOSITION AND APPLICATION OF STATISTICAL TOOLS

We transpose the dataset since having the genes in the colums and the RNA strands in the rows is more intresting under a statistical viewpoint.

In [ ]:
#indeed looking at the transposed dataframe is more useful as genes are in the columns
df_HCC1806_filtered_T = df_HCC1806_filtered.T
df_HCC1806_filtered_summary2 = df_HCC1806_filtered_T.describe()
df_HCC1806_filtered_summary2
"H1-5" "MALAT1" "MT-RNR2" "ARVCF" "BCYRN1" "ATXN7L2" "IGFBP3" "H1-3" "CTIF" "RNF123" ... "BATF3" "CDKN3" "DLD" "PMPCA" "ZNF165" "SCCPDH" "NTAN1" "CLIP2" "DUSP23" "ZNF682"
count 14682.000000 14682.000000 14682.000000 14682.000000 14682.000000 14682.000000 14682.000000 14682.000000 14682.000000 14682.000000 ... 14682.000000 14682.000000 14682.000000 14682.000000 14682.000000 14682.000000 14682.000000 14682.000000 14682.000000 14682.000000
mean 0.709372 1.899333 0.861327 0.001294 1.038142 0.001498 0.371067 0.675862 0.000613 0.000613 ... 0.000477 0.045498 0.026563 0.010693 0.004155 0.009672 0.006471 0.002997 0.019003 0.000477
std 1.680187 2.790889 1.470822 0.047393 1.634813 0.052176 0.905313 1.292052 0.027366 0.027366 ... 0.024755 0.239997 0.189220 0.121667 0.073238 0.114546 0.090554 0.064917 0.156962 0.024755
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
75% 1.000000 3.000000 1.000000 0.000000 2.000000 0.000000 0.000000 1.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
max 58.000000 110.000000 23.000000 4.000000 33.000000 3.000000 14.000000 22.000000 2.000000 2.000000 ... 2.000000 4.000000 4.000000 5.000000 3.000000 3.000000 3.000000 4.000000 5.000000 2.000000

8 rows × 3000 columns

We can see that the mean of most genes is across zero. This was predictable as the sparcity analysis told us that the 97,6% of the entries is 0.

We then plot the 25/50/75% and we can clearly see that the non-zero genes are almost all located in the 75% quartile.

In [ ]:
#we make a histogram of the mean of the expression of the genes
sns.displot(df_HCC1806_filtered_summary2.loc['mean'], bins=30, kde=True)
plt.show()
No description has been provided for this image
In [ ]:
#we make a histogram of the standard deviation of the expression of the genes
sns.displot(df_HCC1806_filtered_summary2.loc['std'], bins=30, kde=True)
plt.show()
No description has been provided for this image
In [ ]:
#we look only at the 25% quantile
sns.displot(df_HCC1806_filtered_summary2.loc['25%'], bins=30, kde=True)
plt.show()
df_HCC1806_filtered_summary2.loc['25%'].value_counts()
No description has been provided for this image 
25%
0.0    2997
1.0       3
Name: count, dtype: int64
In [ ]:
#we look only at the 50% quantile
sns.displot(df_HCC1806_filtered_summary2.loc['50%'], bins=30, kde=True)
plt.show()
df_HCC1806_filtered_summary2.loc['50%'].value_counts()
No description has been provided for this image 
50%
0.0    2986
1.0      11
2.0       2
3.0       1
Name: count, dtype: int64
In [ ]:
#we look only at the 75% quantile
sns.displot(df_HCC1806_filtered_summary2.loc['75%'], bins=30, kde=True)
plt.show()
df_HCC1806_filtered_summary2.loc['75%'].value_counts()
No description has been provided for this image 
75%
0.0    2924
1.0      61
2.0      11
4.0       2
3.0       1
5.0       1
Name: count, dtype: int64

¶

3.2.3) LOOKING FOR MOST USEFUL AND EXPRESSED GENES ACROSS CELLS

Since the mean of most of the genes as seen before is near zero, we restrict our search to the genes that have at least a mean expression of 1.

Also for what regards the standard deviation we restrict our search to the genes that have a standard deviation higher 1.

We split the dataset into two parts containing normoxic RNA strands and hypoxic RNA strands.

In [ ]:
#i want to split the dataset in 2 parts, one with RNA with hypoxia in the name and the other with RNA with normoxia in the name
hypoxia_samples = df_HCC1806_filtered.T[df_HCC1806_filtered.T.index.str.contains('Hypoxia')]
normoxia_samples = df_HCC1806_filtered.T[df_HCC1806_filtered.T.index.str.contains('Normoxia')]
In [ ]:
hypoxia_samples.describe()
"H1-5" "MALAT1" "MT-RNR2" "ARVCF" "BCYRN1" "ATXN7L2" "IGFBP3" "H1-3" "CTIF" "RNF123" ... "BATF3" "CDKN3" "DLD" "PMPCA" "ZNF165" "SCCPDH" "NTAN1" "CLIP2" "DUSP23" "ZNF682"
count 8899.000000 8899.000000 8899.000000 8899.000000 8899.000000 8899.000000 8899.000000 8899.000000 8899.000000 8899.000000 ... 8899.000000 8899.000000 8899.000000 8899.000000 8899.000000 8899.000000 8899.000000 8899.000000 8899.000000 8899.000000
mean 0.659512 1.640634 0.805933 0.001910 0.660411 0.000562 0.558602 0.710305 0.000674 0.000562 ... 0.000337 0.044612 0.022587 0.008316 0.003596 0.009776 0.006967 0.001798 0.018654 0.000674
std 1.556327 2.488606 1.396132 0.058994 1.084628 0.035156 1.088374 1.354096 0.029977 0.028042 ... 0.018359 0.244813 0.178169 0.097959 0.073359 0.119068 0.096911 0.044941 0.164541 0.029977
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
75% 1.000000 2.000000 1.000000 0.000000 1.000000 0.000000 1.000000 1.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
max 38.000000 32.000000 23.000000 4.000000 13.000000 3.000000 14.000000 22.000000 2.000000 2.000000 ... 1.000000 4.000000 3.000000 2.000000 3.000000 3.000000 3.000000 2.000000 5.000000 2.000000

8 rows × 3000 columns

In [ ]:
normoxia_samples.describe()
"H1-5" "MALAT1" "MT-RNR2" "ARVCF" "BCYRN1" "ATXN7L2" "IGFBP3" "H1-3" "CTIF" "RNF123" ... "BATF3" "CDKN3" "DLD" "PMPCA" "ZNF165" "SCCPDH" "NTAN1" "CLIP2" "DUSP23" "ZNF682"
count 5783.000000 5783.000000 5783.000000 5783.000000 5783.000000 5783.00000 5783.000000 5783.000000 5783.000000 5783.000000 ... 5783.000000 5783.000000 5783.000000 5783.000000 5783.000000 5783.000000 5783.000000 5783.000000 5783.000000 5783.000000
mean 0.786097 2.297423 0.946568 0.000346 1.619402 0.00294 0.082483 0.622860 0.000519 0.000692 ... 0.000692 0.046861 0.032682 0.014352 0.005015 0.009511 0.005706 0.004842 0.019540 0.000173
std 1.852219 3.159882 1.575193 0.018595 2.101921 0.07076 0.347361 1.188442 0.022772 0.026293 ... 0.032206 0.232406 0.204930 0.150985 0.073050 0.107225 0.079791 0.087100 0.144537 0.013150
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 0.000000 1.000000 0.000000 0.000000 1.000000 0.00000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
75% 1.000000 3.000000 1.000000 0.000000 2.000000 0.00000 0.000000 1.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
max 58.000000 110.000000 23.000000 1.000000 33.000000 3.00000 8.000000 22.000000 1.000000 1.000000 ... 2.000000 4.000000 4.000000 5.000000 2.000000 3.000000 2.000000 4.000000 2.000000 1.000000

8 rows × 3000 columns

In [ ]:
#we look at how many genes have a mean of expression greater than 1 and we print their names
#for hypoxia samples    
hypoxia_samples_summary = hypoxia_samples.describe()

hypo_genes = hypoxia_samples_summary.loc['mean'][hypoxia_samples_summary.loc['mean'] > 1]
hypo_genes_sorted = sorted(hypo_genes.items(), key=lambda x: x[1], reverse=True)
print(hypo_genes_sorted)
print(f"{len(hypo_genes_sorted)} genes have a mean of expression greater than 1 in hypoxia samples")

## Now we apply the same with the standard deviation
hypo_genes_std = hypoxia_samples_summary.loc['std'][hypoxia_samples_summary.loc['std'] > 1]
hypo_genes_std_sorted = sorted(hypo_genes_std.items(), key=lambda x: x[1], reverse=True)
print(hypo_genes_std_sorted)
print(f"{len(hypo_genes_std_sorted)} genes have a standard deviation greater than 1 in hypoxia samples")

[('"H4C3"', 3.118889762894707), ('"FTL"', 3.0734914035284864), ('"RPS5"', 2.4254410607933474), ('"ENO1"', 1.7242386785032027), ('"MALAT1"', 1.6406337790763008), ('"HSP90B1"', 1.6295089335880435), ('"RPS28"', 1.5935498370603438), ('"RPL41"', 1.4645465782672211), ('"RPL8"', 1.213282391279919), ('"HSP90AA1"', 1.185975952354197), ('"HSP90AB1"', 1.1049556129902236)]
11 genes have a mean of expression greater than 1 in hypoxia samples
[('"H4C3"', 2.8476371578300013), ('"FTL"', 2.5232552155263117), ('"MALAT1"', 2.4886062420184993), ('"RPS5"', 2.137477715655018), ('"HSP90B1"', 2.0808422766117287), ('"ENO1"', 1.8570297304465508), ('"RPS28"', 1.6715214669450547), ('"H1-5"', 1.5563269417106602), ('"RPL41"', 1.531770551050613), ('"MT-RNR2"', 1.3961321004928406), ('"RPL8"', 1.372234955432059), ('"HSP90AA1"', 1.363856478982739), ('"H1-3"', 1.3540962502110523), ('"CALR"', 1.3255514472481256), ('"HSP90AB1"', 1.318641158199809), ('"RPL35"', 1.255768175970864), ('"RPLP2"', 1.253896852848974), ('"RPS2"', 1.1714264035549466), ('"RPLP1"', 1.1481861821292367), ('"NCL"', 1.1467903607169887), ('"RPS12"', 1.1333494220241271), ('"HSPA5"', 1.1223971895645422), ('"RPS19"', 1.0984475361923334), ('"IGFBP3"', 1.0883735997373036), ('"BCYRN1"', 1.0846278152986264), ('"HNRNPU"', 1.0695292597243782), ('"RPS27"', 1.0488082810291641), ('"LDHA"', 1.0389707223698534), ('"YBX1"', 1.038023909272488), ('"SERF2"', 1.0370512555557534), ('"EZR"', 1.0325425277623), ('"RPS15"', 1.0294956691922739), ('"RPS8"', 1.0158719090842991)]
33 genes have a standard deviation greater than 1 in hypoxia samples

We can see that in hypoxic RNA strands most of the genes that have a mean higher than 1 have also the standard deviation higher than 1, in fact, up to permutations of the position, the first 5 genes are the same.

In [ ]:
normoxia_samples_summary = normoxia_samples.describe()

normo_genes = normoxia_samples_summary.loc['mean'][normoxia_samples_summary.loc['mean'] > 1]
normo_genes_sorted = sorted(normo_genes.items(), key=lambda x: x[1], reverse=True)
print(normo_genes_sorted)
print(f" {len(normo_genes_sorted)} genes have a mean of expression greater than 92.814 in normoxia samples")

normo_genes_std = normoxia_samples_summary.loc['std'][normoxia_samples_summary.loc['std'] > 1]
normo_genes_std_sorted = sorted(normo_genes_std.items(), key=lambda x: x[1], reverse=True)
print(normo_genes_std_sorted)
print(f"{len(normo_genes_std_sorted)} genes have a standard deviation greater than 75.526 in normoxia samples")

[('"H4C3"', 3.745979595365727), ('"FTL"', 3.056890887082829), ('"RPS5"', 2.897458066747363), ('"MALAT1"', 2.2974234826214768), ('"HSP90AA1"', 2.1028877745114993), ('"HSP90AB1"', 1.8661594328203355), ('"BCYRN1"', 1.6194016946221683), ('"NCL"', 1.426941034065364), ('"HSP90B1"', 1.3441120525678714), ('"RPL8"', 1.2953484350683035), ('"RPL35"', 1.2038734220992564), ('"ENO1"', 1.1971295175514438), ('"HSPD1"', 1.1625453916652257), ('"RPS28"', 1.0747017119142315), ('"AKR1C2"', 1.0050146982535015)]
 15 genes have a mean of expression greater than 92.814 in normoxia samples
[('"H4C3"', 3.576264136244412), ('"MALAT1"', 3.1598821180514505), ('"FTL"', 2.8649494710788774), ('"RPS5"', 2.5193884163947264), ('"BCYRN1"', 2.1019214714967513), ('"H1-5"', 1.8522193169345953), ('"HSP90AB1"', 1.7330693785722528), ('"HSP90AA1"', 1.671157537583227), ('"MT-RNR2"', 1.5751930127971736), ('"HSP90B1"', 1.489570394829847), ('"ENO1"', 1.4844560903313344), ('"NCL"', 1.4798825401244873), ('"RPL35"', 1.3296653334142248), ('"RPL8"', 1.3152081767018833), ('"RPS28"', 1.308364441984559), ('"AKR1C2"', 1.2363998695618124), ('"HSPD1"', 1.2169680764817927), ('"CALR"', 1.2055489889868944), ('"HSPA5"', 1.189530811541049), ('"H1-3"', 1.1884415303903189), ('"HNRNPU"', 1.1503620069188694), ('"HNRNPA2B1"', 1.1283381343863612), ('"RPLP2"', 1.0840653425607143), ('"RPL41"', 1.0746106310381314), ('"RPS12"', 1.0547394319874752), ('"EZR"', 1.0358439770900387), ('"CALM2"', 1.0065949050864629)]
27 genes have a standard deviation greater than 75.526 in normoxia samples

Also for normoxic RNA strands most of the genes that have a mean higher than 1 have also the standard deviation higher than 1.

In [ ]:
# Transpose the DataFrames
hypoxia_summary_t = hypoxia_samples_summary.transpose()
normoxia_summary_t = normoxia_samples_summary.transpose()

# Calculate the difference in means
diff_mean = abs(hypoxia_summary_t['mean'] - normoxia_summary_t['mean'])

# Sort genes by difference in mean in descending order
sorted_genes = diff_mean.sort_values(ascending=False)

# Get the list of genes
genes_list = sorted_genes.index.tolist()
genes_list

# Create a DataFrame from the sorted series
sorted_genes_df = sorted_genes.reset_index()

# Rename the columns
sorted_genes_df.columns = ['Gene', 'Difference']

# Display the DataFrame
sorted_genes_df[:50]
Gene Difference
0 "BCYRN1" 0.958990
1 "HSP90AA1" 0.916912
2 "HSP90AB1" 0.761204
3 "MALAT1" 0.656790
4 "AKR1C2" 0.654526
5 "H4C3" 0.627090
6 "NCL" 0.620671
7 "HSPD1" 0.602707
8 "RPL41" 0.599943
9 "ENO1" 0.527109
10 "RPS28" 0.518848
11 "LDHA" 0.490320
12 "IGFBP3" 0.476119
13 "RPS5" 0.472017
14 "FGF23" 0.444528
15 "WDR43" 0.443098
16 "TMSB10" 0.383676
17 "GPM6A" 0.380905
18 "NDRG1" 0.365190
19 "AKR1C1" 0.364441
20 "CALM2" 0.354971
21 "S100A10" 0.348586
22 "EIF3J" 0.316021
23 "KRT19" 0.286232
24 "HSP90B1" 0.285397
25 "RPLP1" 0.285041
26 "RPS27" 0.282417
27 "HNRNPU" 0.259843
28 "S100A11" 0.253016
29 "SERF2" 0.252565
30 "RPL35" 0.219830
31 "ZC3H15" 0.210185
32 "YWHAZ" 0.209874
33 "NPM1P40" 0.207242
34 "P4HA1" 0.204846
35 "CAST" 0.199762
36 "CACNA1A" 0.196696
37 "RPL39" 0.194336
38 "S100A6" 0.193717
39 "B4GALT1" 0.190929
40 "RPS3" 0.177411
41 "NCALD" 0.176731
42 "PGK1" 0.173551
43 "PSMA7" 0.170456
44 "GAPDH" 0.167722
45 "EEF2" 0.158171
46 "RPL10" 0.149857
47 "HNRNPA2B1" 0.147387
48 "RPL27A" 0.146624
49 "TMSB4X" 0.146578
In [ ]:
#we do it also for normoxia samples
normoxia_samples_summary = normoxia_samples.describe()
normo_genes = normoxia_samples_summary.loc['mean'][normoxia_samples_summary.loc['mean'] > 1]
normo_genes 

"MALAT1"      2.297423
"BCYRN1"      1.619402
"H4C3"        3.745980
"HSP90B1"     1.344112
"FTL"         3.056891
"ENO1"        1.197130
"AKR1C2"      1.005015
"RPS28"       1.074702
"RPS5"        2.897458
"NCL"         1.426941
"RPL35"       1.203873
"HSP90AB1"    1.866159
"HSPD1"       1.162545
"HSP90AA1"    2.102888
"RPL8"        1.295348
Name: mean, dtype: float64
In [ ]:
#we look at the mean of the gene "BCYRN1" for hypoxia samples
hypoxia_samples_summary.loc['mean']['"BCYRN1"']

0.6604112821665356
In [ ]:
#we look at the mean of the gene "RPL41" for normoxia samples
normoxia_samples_summary.loc['mean']['"RPL41"']

0.8646031471554556

We inspect the functions of the genes that seem to have the highest difference in mean between the two samples:

We see that as in the MCF7 cells also for this one we find a under-regulation of MALAT1 gene.

Also for what regards the gene BCYRN1, we see that it is very under-regulated in hypoxia samples, in this case there isn't direct evidence suggesting that BCYRN1 is under-regulated in cells in hypoxia. However, given that hypoxia can induce widespread changes in gene expression and cellular processes, it's plausible that BCYRN1 expression could be affected indirectly.

Moreover, the gene RPL41 is upregulated in hypoxia samples, this can be meaningful as in some studies, hypoxia has been shown to induce the upregulation of ribosomal protein genes, including RPL41, as part of the cellular response to low oxygen levels.

Finally, also for what regards the HSP90AA1 gene, we see that it is under-regulated in hypoxia samples, and it's a good discovery as there is also evidence suggesting that HSP90AA1 expression can be downregulated in hypoxia. Some studies have reported decreased levels of HSP90AA1 mRNA and protein under prolonged or severe hypoxic conditions.

In [ ]:
## Now with the std
# Transpose the DataFrames
hypoxia_summary_t = hypoxia_samples_summary.transpose()
normoxia_summary_t = normoxia_samples_summary.transpose()

# Calculate the difference in means
diff_std = abs(hypoxia_summary_t['std'] - normoxia_summary_t['std'])

# Sort genes by difference in mean in descending order
sorted_genes_std = diff_std.sort_values(ascending=False)

# Get the list of genes
genes_list = sorted_genes_std.index.tolist()
genes_list

# Create a DataFrame from the sorted series
sorted_genes_df = sorted_genes_std.reset_index()

# Rename the columns
sorted_genes_df.columns = ['Gene', 'Difference']

# Display the DataFrame
sorted_genes_df[:50]
Gene Difference
0 "BCYRN1" 1.017294
1 "IGFBP3" 0.741012
2 "H4C3" 0.728627
3 "MALAT1" 0.671276
4 "NDRG1" 0.608544
5 "HSP90B1" 0.591272
6 "FGF23" 0.538811
7 "AKR1C2" 0.517095
8 "LDHA" 0.505089
9 "GPM6A" 0.496045
10 "RPL41" 0.457160
11 "TMSB10" 0.455659
12 "HSP90AB1" 0.414428
13 "S100A10" 0.399990
14 "P4HA1" 0.383122
15 "RPS5" 0.381911
16 "AKR1C1" 0.379500
17 "ENO1" 0.372574
18 "RPS28" 0.363157
19 "RPS27" 0.345836
20 "FTL" 0.341694
21 "NCL" 0.333092
22 "BNIP3" 0.329407
23 "NPM1P40" 0.325089
24 "B4GALT1" 0.324425
25 "KRT19" 0.320878
26 "S100A11" 0.318739
27 "NCALD" 0.314658
28 "CACNA1A" 0.312250
29 "HSP90AA1" 0.307301
30 "RPLP1" 0.307264
31 "HSPD1" 0.306199
32 "H1-5" 0.295892
33 "RPL39" 0.294623
34 "PGK1" 0.292165
35 "MT-TQ" 0.289243
36 "WDR43" 0.286854
37 "BTBD9" 0.283565
38 "S100A6" 0.278838
39 "SERF2" 0.277494
40 "NEAT1" 0.274250
41 "EGLN3" 0.272631
42 "CAST" 0.266825
43 "H2AC12" 0.260353
44 "RPS3" 0.254830
45 "H4C5" 0.252904
46 "ZNF302" 0.249627
47 "PRRG3" 0.241393
48 "RPL10" 0.236367
49 "MT-TV" 0.232770

Even studying the std,the results are confirmed, genes in the firsts position are similar to before.

¶

3.2.4) UNSUPERVISD LEARNING APPLIED TO MCF7

We firstly decided to do a PCA of two components and then to perform a kmeans clustering.

Also here the result was bad because, as can be seen from the first picture, the datapoints were not linearly separable.

The performance of the kmins in fact another time just a division in two parts of the graph.

In [ ]:
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
import re
def extract_condition(name):
    match = re.search(r'Normoxia|Hypoxia', name)
    if match:
        return match.group()
    return 'unknown'


pca = PCA(n_components=2)
principalComponents = pca.fit_transform(df_HCC1806_filtered_T)

k = 2
kmeans = KMeans(n_clusters=k, init='k-means++',random_state=0, n_init=10)
clusters = kmeans.fit_predict(principalComponents)
In [ ]:
plt.figure(figsize=(12, 6))

# Assign colors to conditions manually
color_map = {'Hypoxia': 'red', 'Normoxia': 'blue', 'Unknown': 'green'}
conditions = df_HCC1806_filtered_T.index.to_series().apply(extract_condition)

colors = conditions.map(color_map)

plt.scatter(principalComponents[:, 0], principalComponents[:, 1], c=colors)
plt.title('PCA of Cells Colored by Condition')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')

# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i], 
            label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')

plt.show()
No description has been provided for this image
In [ ]:
# Visualization of PCA with K-means clusters, with a proper legend
plt.figure(figsize=(12, 6))

# Assign colors based on the K-means cluster result
cluster_colors = ['green' if label == 0 else 'yellow' for label in clusters]

# Scatter plot with assigned colors
scatter = plt.scatter(principalComponents[:, 0], principalComponents[:, 1], c=cluster_colors)

# Create a legend for the colors
plt.title('PCA of Cells Colored by K-means Clusters')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')

legend_labels = {0: 'Cluster 1', 1: 'Cluester 2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
                      markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='Division')

plt.show()
No description has been provided for this image

Then we tried to apply a PCA of 3 components and then the kmins clustering.

Also in this case the datapoints were not linearly separable and thus the performance of the kmins was terrible.

In [ ]:
#now i want to do a pca with 3 components
pca = PCA(n_components=3)
principalComponents3 = pca.fit_transform(df_HCC1806_filtered_T)
clusters3 = kmeans.fit_predict(principalComponents3)
In [ ]:
# Visualization of PCA in 3 dimensions, with a proper legend

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(111, projection='3d')


# Assign colors to conditions manually
colors = conditions.map(color_map)

# Scatter plot with assigned colors
scatter = ax.scatter(principalComponents3[:, 0], principalComponents3[:, 1], principalComponents3[:, 2], c=colors)

# Create a legend for the colors
ax.set_title('PCA of Cells Colored by Condition')
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')

# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i], 
            label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')

plt.show()
No description has been provided for this image
In [ ]:
# Visualization of PCA with K-means clusters, with a proper legend
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(111, projection='3d')

# Assign colors based on the K-means cluster result
cluster_colors3 = ['green' if label == 0 else 'yellow' for label in clusters3]

# Scatter plot with assigned colors
scatter = ax.scatter(principalComponents3[:, 0], principalComponents3[:, 1], principalComponents3[:, 2], c=cluster_colors3)

# Create a legend for the colors
ax.set_title('PCA of Cells Colored by K-means Clusters')
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')

# Assuming that after any potential inversion, cluster 0 corresponds to Normoxia and cluster 1 to Hypoxia
legend_labels = {0: 'Cluster 1', 1: 'Cluster 2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
                      markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='division')

plt.show()
No description has been provided for this image

Also for this dataset we then tried to apply the t-Distributed Stochastic Neighbor Embedding (t-SNE) to our data.

But also in this case the result was not good, both in the 2-dimensional and 3-dimensional case the datapoints were concentrated and not separable.

Thus the performance of k-mins was bad.

In [ ]:
from sklearn.manifold import TSNE

# Dimensionality reduction with t-SNE
tsne3 = TSNE(n_components=3)
embedded_data3 = tsne3.fit_transform(df_HCC1806_filtered_T)

# Apply k-means clustering to the t-SNE result
k = 2
kmeans = KMeans(n_clusters=k, init='k-means++',random_state=0, n_init=10)
clustersT3 = kmeans.fit_predict(embedded_data3)


# Visualization of t-SNE in 3 dimensions, with a proper legend
fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(111, projection='3d')

# Assign colors to conditions manually
colors = conditions.map(color_map)

# Scatter plot with assigned colors
scatter = ax.scatter(embedded_data3[:, 0], embedded_data3[:, 1], embedded_data3[:, 2], c=colors)

# Create a legend for the colors
ax.set_title('t-SNE of Cells Colored by Condition')
ax.set_xlabel('t-SNE Dimension 1')
ax.set_ylabel('t-SNE Dimension 2')
ax.set_zlabel('t-SNE Dimension 3')

# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i], 
            label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')

plt.show()
No description has been provided for this image
In [ ]:
#plot the clusters

fig = plt.figure(figsize=(12, 6))
ax = fig.add_subplot(111, projection='3d')

# Assign colors based on the Spectral Clustering result
cluster_colorsT3 = ['green' if label == 0 else 'yellow' for label in clustersT3]

# Scatter plot with assigned colors
scatter = ax.scatter(embedded_data3[:, 0], embedded_data3[:, 1], embedded_data3[:, 2], c=cluster_colorsT3)

# Create a legend for the colors
ax.set_title('t-SNE of Cells Colored by Kmeans Clusters')
ax.set_xlabel('t-SNE Dimension 1')
ax.set_ylabel('t-SNE Dimension 2')
ax.set_zlabel('t-SNE Dimension 3')

legend_labels = {0: 'Cluster 1', 1: 'Cluster 2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
                      markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='Division')

plt.show()
No description has been provided for this image
In [ ]:
from sklearn.manifold import TSNE

# Dimensionality reduction with t-SNE
tsne2 = TSNE(n_components=2)
embedded_data2 = tsne2.fit_transform(df_HCC1806_filtered_T)

plt.figure(figsize=(12, 6))

#apply kmeans clustering
k = 2
kmeans = KMeans(n_clusters=k, init='k-means++',random_state=0, n_init=10)
clustersT2 = kmeans.fit_predict(embedded_data2)


# Assign colors to conditions manually
color_map = {'Hypoxia': 'red', 'Normoxia': 'blue', 'Unknown': 'green'}
colors = conditions.map(color_map)

plt.scatter(embedded_data2[:, 0], embedded_data2[:, 1], c=colors)
plt.title('t-SNE of Cells Colored by Condition')
plt.xlabel('t-SNE Component 1')
plt.ylabel('t-SNE Component 2')

# Create a legend for the colors
patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=color_map[i], 
            label="{:s}".format(i))[0] for i in color_map]
plt.legend(handles=patches, loc='best', title='Condition')

plt.show()
No description has been provided for this image
In [ ]:
#plot the clusters

plt.figure(figsize=(12, 6))

# Assign colors based on the Spectral Clustering result
cluster_colors2 = ['green' if label == 0 else 'yellow' for label in clustersT2]

# Scatter plot with assigned colors
scatter = plt.scatter(embedded_data2[:, 0], embedded_data2[:, 1], c=cluster_colors2)

# Create a legend for the colors
plt.title('t-SNE of Cells Colored by Kmeans Clusters')
plt.xlabel('t-SNE Component 1')
plt.ylabel('t-SNE Component 2')

legend_labels = {0: 'cluster 1', 1: 'cluster 2'}
handles = [plt.Line2D([0], [0], marker='o', color='w', label=legend_labels[label],
                      markersize=10, markerfacecolor=clr) for label, clr in zip([0, 1], ['green', 'yellow'])]
plt.legend(handles=handles, title='Division')

plt.show()
No description has been provided for this image

For the sake of curiosity, also in this case we finally tried to perform a "manual" PCA selecting 3 of the genes that where found as importantin the previous analysis.

But also the result we found was not separable and thus the performance of kmins was also in this case bad.

In [ ]:
selected_genes = ['"BCYRN1"', '"HSP90AA1"', '"MALAT1"']
selected_genes_df = df_HCC1806_filtered.loc[selected_genes]

# Transpose the DataFrame
selected_genes_df = selected_genes_df.T

# Apply PCA to the selected genes
pca = PCA(n_components=3)
selected_genes_transformed = pca.fit_transform(selected_genes_df)

# Assign colors to conditions manually
conditions = selected_genes_df.index.to_series().apply(extract_condition)
color_map = {'Hypoxia': 'red', 'Normoxia': 'blue'}
colors = conditions.map(color_map)

# Create a 3D scatter plot
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(selected_genes_transformed[:, 0], selected_genes_transformed[:, 1], selected_genes_transformed[:, 2], c=colors)
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
plt.title('PCA of Selected Genes (MT_RNR2, TFF1, MALAT1) Colored by Condition')
plt.show()
No description has been provided for this image
In [ ]:
kmeans = KMeans(n_clusters=2, random_state=42)
clusters1 = kmeans.fit_predict(selected_genes_transformed)

# Assign colors to conditions manually
conditions = selected_genes_df.index.to_series().apply(extract_condition)
color_map = {'Hypoxia': 'green', 'Normoxia': 'yellow'}
colors = conditions.map(color_map)
cluster_colorsH = ['yellow' if label == 0 else 'green' for label in clusters1]

# Create a 3D scatter plot
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(selected_genes_transformed[:, 0], selected_genes_transformed[:, 1], selected_genes_transformed[:, 2], c=cluster_colorsH)
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.set_zlabel('Principal Component 3')
plt.title('K-means Clustering of Selected Genes (GAPDH, ALDOA, PGK1)')
plt.show()

/Users/teodorotoscano/opt/anaconda3/lib/python3.8/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
No description has been provided for this image

¶

3.2.5) More unsupervised learning

In [ ]:
X_hcc_drop = df_trans_hcc_drop.drop('TARGET', axis=1)
y_train_hcc_drop=df_trans_hcc_drop['TARGET']

scaler_hcc_drop = StandardScaler()
X_scaled_hcc_drop = scaler_hcc_drop.fit_transform(X_hcc_drop)
In [ ]:
pca2_hcc_drop = PCA(n_components=2)
X_pca2_hcc_drop = pca2_hcc_drop.fit_transform(X_scaled_hcc_drop)
In [ ]:
plt.figure(figsize=(12, 6))

plt.scatter(X_pca2_hcc_drop[:,0][y_train_hcc_drop==1], X_pca2_hcc_drop[:,1][y_train_hcc_drop==1], c='r')
plt.scatter(X_pca2_hcc_drop[:,0][y_train_hcc_drop==0], X_pca2_hcc_drop[:,1][y_train_hcc_drop==0], c='b')

plt.title("Clusterization PCA hcc_drop")

plt.show()
No description has been provided for this image
In [ ]:
'''% of EXPLAINED VARIANCE'''
pca_hcc_drop = PCA().fit(X_scaled_hcc_drop)
In [ ]:
explained_variance_ratio_hcc_drop = pca_hcc_drop.explained_variance_ratio_
cumulative_variance_hcc_drop = explained_variance_ratio_hcc_drop.cumsum()

plt.plot(range(1, len(cumulative_variance_hcc_drop) + 1), cumulative_variance_hcc_drop, marker='o')
plt.xlabel("Number of components")
plt.ylabel("Cumulative proportion of Variance Explained")
plt.title("Analysis of explained variance")
plt.show()
No description has been provided for this image
In [ ]:
tsne_model_hcc_drop = TSNE(n_components=2) #, perplexity=10
X_tsne_hcc_drop = tsne_model_hcc_drop.fit_transform(X_scaled_hcc_drop)
In [ ]:
# Visualization of results
plt.figure(figsize=(12, 6))
# plt.scatter(X_train_pca[:,0], X_train_pca[:,1], cmap='viridis')


plt.scatter(X_tsne_hcc_drop[:,0][y_train_hcc_drop==1], X_tsne_hcc_drop[:,1][y_train_hcc_drop==1], c='r')
plt.scatter(X_tsne_hcc_drop[:,0][y_train_hcc_drop==0], X_tsne_hcc_drop[:,1][y_train_hcc_drop==0], c='b')

plt.title("Clusterization TSNE hcc_drop")

plt.show()
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¶

  1. Classifiers

¶

4.0) Definitions

¶

4.0.1) Definition of Neural Networks

We defined four different neural networks models based on the dataset we used as input to train them.

¶

4.0.1.1) Definition of the neural network on single dataset (dropseq)

This neural network called "NeuralNetworkDropSeq" is the one we used with both the datasets DropSeq MCF7 and HCC1806 separately. The neural network has the following structure:

  • a 'Flatten' layer to transform the input data into a one-dimensional array;
  • a series of 'Linear' layers that progressively reduce in size: the first layer takes 3000 input features (from the MCF7 or HCC1806 data) and connects to 512 neurons, followed by reductions from 512 to 256, 256 to 128, and finally 128 to 64 neurons;
  • 'LeakyReLU' activation is used after each Linear layer to introduce non-linearity;
  • 'Dropout' layers are inserted after the first, second, and third 'LeakyReLU' activations to prevent overfitting by randomly setting 40% of the inputs to zero after the first and second layers, and 30% after the third layer;
  • a 'BatchNorm1d' layer follows the second 'LeakyReLU' to normalize the activations from the second Linear layer, also aiding in reducing overfitting; The final output is through a single neuron that predicts a binary value (1 or 0) representing the state of the cell (normoxia or hypoxia)
In [ ]:
class NeuralNetworkDropSeq(nn.Module):
    def __init__(self):
        super(NeuralNetworkDropSeq, self).__init__()
        self.model = nn.Sequential(
            nn.Flatten(),
            nn.Linear(3000, 512),  # Increased layer size
            nn.LeakyReLU(),
            nn.Dropout(0.4),
            
            nn.Linear(512, 256),  # Increased layer size
            nn.LeakyReLU(),
            nn.BatchNorm1d(256),
            nn.Dropout(0.4),
            
            nn.Linear(256, 128),  # Increased layer size
            nn.LeakyReLU(),
            nn.Dropout(0.3),
            
            nn.Linear(128, 64),  # Kept the original layer
            nn.LeakyReLU(),
            
            nn.Linear(64, 1)  # Output layer
        )
    
    def forward(self, x):
        return self.model(x)

¶

4.0.1.2) Definition on merged dropseq

The following neural network called "NeuralNetworkDropSeqMerged" is the one we used with the merged datasets MCF7 and HCC1806. It has the same structure discussed before with the difference of the first linear layer which contains 5166 neurons (the reason is because the merged dataset has 5166 features).

In [ ]:
class NeuralNetworkDropSeqMerged(nn.Module):
    def __init__(self):
        super(NeuralNetworkDropSeqMerged, self).__init__()
        self.model = nn.Sequential(
            nn.Flatten(),
            nn.Linear(5166, 512),  # Increased layer size
            nn.LeakyReLU(),
            nn.Dropout(0.4),
            
            nn.Linear(512, 256),  # Increased layer size
            nn.LeakyReLU(),
            nn.BatchNorm1d(256),
            nn.Dropout(0.4),
            
            nn.Linear(256, 128),  # Increased layer size
            nn.LeakyReLU(),
            nn.Dropout(0.3),
            
            nn.Linear(128, 64),  # Kept the original layer
            nn.LeakyReLU(),
            
            nn.Linear(64, 1)  # Output layer
        )
    
    def forward(self, x):
        return self.model(x)

¶

4.0.1.3) Definition on general (all 4 dataset merged)

Finally, the last neural network we built, called "GP1" (general predictor), is the one we used with the general dataset (with features from both smartest and dropseq) and again has the same structure apart in the first layer where we have 8645 neurons.

In [ ]:
class GP1(nn.Module):
    def __init__(self):
        super(GP1, self).__init__()
        self.model = nn.Sequential(
            nn.Flatten(),
            nn.Linear(8645, 512),  # Increased layer size
            nn.LeakyReLU(),
            nn.Dropout(0.4),
            
            nn.Linear(512, 256),  # Increased layer size
            nn.LeakyReLU(),
            nn.BatchNorm1d(256),
            nn.Dropout(0.4),
            
            nn.Linear(256, 128),  # Increased layer size
            nn.LeakyReLU(),
            nn.Dropout(0.3),
            
            nn.Linear(128, 64),  # Kept the original layer
            nn.LeakyReLU(),
            
            nn.Linear(64, 1)  # Output layer
        )
    
    def forward(self, x):
        return self.model(x)

¶

4.0.2) Definition of KNN

In [ ]:
def train_and_evaluate_knn(X_train, X_test, y_train, y_test):
    #KNN model creation  
    neigh = KNeighborsClassifier()
    #define CV strategy
    cv = StratifiedKFold(4)
    #we are going to optimize only one hyperparameter
    param_grid = {'n_neighbors': [2,3,4]}
    # lets train and optimize model
    grid_search = GridSearchCV(neigh, param_grid, cv=cv)
    grid_search.fit(X_train, y_train)
    #get the best one
    final_model = grid_search.best_estimator_

    y_pred = final_model.predict(X_test)
    print("Best hyperparameters: ", grid_search.best_params_)
    print("Accuracy:", accuracy_score(y_test, y_pred))
    print("F1:", f1_score(y_test, y_pred))
    print("Classification Report:")
    print(classification_report(y_test, y_pred))
    return final_model

¶

4.0.3) Definition Logistic Regression

In [ ]:
def train_and_evaluate_logreg(X_train, X_test, y_train, y_test):
    #log reg model creation  
    logreg = LogisticRegression()
    #define CV strategy
    cv = StratifiedKFold(4)
    #we are going to optimize only one hyperparameter
    param_grid = {'C': [0.0001,0.002,0.005,0.007,0.01,0.01,0.03,0.1,0.5],'penalty': ['l2']}
    # lets train and optimize model
    grid_search = GridSearchCV(logreg, param_grid, cv=cv)
    grid_search.fit(X_train, y_train)
    #get the best one
    final_model = grid_search.best_estimator_
    
    y_pred = final_model.predict(X_test)
    print("Best hyperparameters: ", grid_search.best_params_)
    print("Accuracy:", accuracy_score(y_test, y_pred))
    print("F1:", f1_score(y_test, y_pred))
    print("Classification Report:")
    print(classification_report(y_test, y_pred))
    return final_model

¶

4.0.4) Definition of CatBoost

In [ ]:
def train_and_evaluate_catboost(X_train, X_test, y_train, y_test):
    #catboost model creation  
    catboost = CatBoostClassifier()
    #define CV strategy
    cv = StratifiedKFold(4)
    #we are going to optimize only one hyperparameter
    param_grid = {
#         'n_estimators': [200,500,750],
#         'l2_leaf_reg': [0,3,10],
#         'learning_rate' : [0.01,0.03,0.05],
        'logging_level': ['Silent']
    }
    # lets train and optimize model
    grid_search = GridSearchCV(catboost, param_grid, cv=cv)
    grid_search.fit(X_train, y_train)
    #get the best one
    final_model = grid_search.best_estimator_
    
    y_pred = final_model.predict(X_test)
    print("Best hyperparameters: ", grid_search.best_params_)
    print("Accuracy:", accuracy_score(y_test, y_pred))
    print("F1:", f1_score(y_test, y_pred))
    print("Classification Report:")
    print(classification_report(y_test, y_pred))
    return final_model



def train_and_evaluate_catboost2(X_train, X_test, y_train, y_test):
    #catboost model creation  
    catboost = CatBoostClassifier(logging_level = 'Silent')
    catboost.fit(X_train,y_train)
    # get features importance
    importance=catboost.get_feature_importance()
    names=X_train.columns

    feature_importance = np.array(importance)
    feature_names = np.array(names)

    #Create a DataFrame using a Dictionary
    data={'feature_names':feature_names,'feature_importance':feature_importance}
    fi_df = pd.DataFrame(data)

    #Sort the DataFrame in order decreasing feature importance
    fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)

    fi_df['acc_sum'] = fi_df.feature_importance.cumsum()
    # get features provided 99% of information for model
    col_cut=list(fi_df[fi_df.acc_sum<=99].feature_names.values)
    print(len(col_cut))
    
    
    catboost = CatBoostClassifier()
    #define CV strategy
    cv = StratifiedKFold(4)
    #we are going to optimize only one hyperparameter
    param_grid = {
            'n_estimators': [500,1000,1500],
            'l2_leaf_reg': [3,6,10],
    #         'learning_rate' : [0.01,0.03,0.05],
        'logging_level': ['Silent']
    }
    # lets train and optimize model
    grid_search = GridSearchCV(catboost, param_grid, cv=cv)
    grid_search.fit(X_train[col_cut], y_train)
    #get the best one
    final_model = grid_search.best_estimator_

    y_pred = final_model.predict(X_test[col_cut])
    print("Best hyperparameters: ", grid_search.best_params_)
    print("Accuracy:", accuracy_score(y_test, y_pred))
    print("F1:", f1_score(y_test, y_pred))
    print("Classification Report:")
    print(classification_report(y_test, y_pred))

    
    return final_model,col_cut

¶

4.0.5) Data transformation

In [ ]:
def prepare_data(df):
    X = df.drop('TARGET', axis=1)
    y = df['TARGET']
    # train test samples
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    # scaler applying 
    scaler = StandardScaler()
    X_train_scaled = scaler.fit_transform(X_train)
    X_test_scaled = scaler.transform(X_test)
    return X_train_scaled, X_test_scaled, y_train, y_test,scaler
In [ ]:
def prepare_data_pca(df):
    X = df.drop('TARGET', axis=1)
    y = df['TARGET']
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    # scaler applying 
    scaler = StandardScaler()
    X_train_scaled = scaler.fit_transform(X_train)
    X_test_scaled = scaler.transform(X_test)
    # pca model, which provides at least 80% of variance  
    pca = PCA(0.8)
    X_train_reduced = pca.fit_transform(X_train_scaled)
    
    X_test_reduced = pca.transform(X_test_scaled)
    
    return X_train_reduced, X_test_reduced, y_train, y_test,scaler,pca

¶

4.1) PREDICTORS SMARTSEQ MCF7

We first start by loading the dataset and then we add to it a 'target' column which contains 1 if the cell is grown in hypoxic conditions and 0 if not.

After that we divide it in the X dataset that contains the features and Y that contains the targets.

Finally, we split it into training and testing data, with a balance of 70% training and 30% testing.

In [ ]:
# load the SmartSeq MCF7 dataset
df_SS_MCF7_norm_filt = pd.read_csv("SmartSeq\MCF7_SmartS_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ",engine='python',index_col=0)

df_SS_MCF7_norm_filtT = df_SS_MCF7_norm_filt.T

# Add the target column
df_SS_MCF7_norm_filtT['target'] = np.where(df_SS_MCF7_norm_filtT.index.str.contains('Hypo'), 1, 0)

#i want to split the data into training and testing data, with a balance of 70% training and 30% testing
X_df_SS_MCF7= df_SS_MCF7_norm_filtT.drop('target', axis=1)
y_df_SS_MCF7 = df_SS_MCF7_norm_filtT['target']

X_train_df_SS_MCF7, X_test_df_SS_MCF7, y_train_df_SS_MCF7, y_test_df_SS_MCF7 = train_test_split(X_df_SS_MCF7, y_df_SS_MCF7, test_size=0.3, random_state=42)
In [ ]:
df_basic_SS_MCF=pd.read_csv("SmartSeq\MCF7_SmartS_Filtered_Normalised_3000_Data_train.txt", delimiter="\ ", engine='python',index_col=0)
df_trans_SS_MCF=df_basic_SS_MCF.T
df_trans_SS_MCF['TARGET'] = df_trans_SS_MCF.columns.to_series().apply(lambda x: 1 if 'Hypo' in x else 0)
df_trans_SS_MCF.loc[df_trans_SS_MCF.index.str.contains('Norm'), 'TARGET'] = 0
df_trans_SS_MCF.loc[df_trans_SS_MCF.index.str.contains('Hypo'), 'TARGET'] = 1
In [ ]:
X_train_SS_MCF, X_test_SS_MCF, y_train_SS_MCF, y_test_SS_MCF,scaler_SS_MCF = prepare_data(df_trans_SS_MCF)

¶

4.1.1) SVM

First predictor we use are SVM. In particular, to find the best estimator we use a parametric grid for GridSearchCV function based on the kernel of the SVM (either linear or RBF). We also perform a 2 fold cross validation.

In [ ]:
# build SVM on SmartSeq MCF7

# Define the SVM classifier
svm = SVC(random_state=42)

# Define the parameter grid for GridSearchCV
param_grid = {'kernel': ['linear', 'rbf']}
grid_search_svm = GridSearchCV(svm, param_grid, cv=2)

# Perform the cross validation
scores_svm = cross_val_score(grid_search_svm, X_train_df_SS_MCF7, y_train_df_SS_MCF7, cv=2)

# Print the mean accuracy of the nested cross validation
print("Mean accuracy of 2 fold cross validation: ", np.mean(scores_svm))

# Perform GridSearchCV on the train dataset to get the best parameters and fit the model
grid_search_svm.fit(X_train_df_SS_MCF7, y_train_df_SS_MCF7)
print("Best hyperparameters: ", grid_search_svm.best_params_)
print("Best accuracy: ", grid_search_svm.best_score_)
best_svm_SS_MCF7 = grid_search_svm.best_estimator_
print("Best model: ", best_svm_SS_MCF7)

# Test the model on the test dataset
print("Test accuracy: ", grid_search_svm.best_estimator_.score(X_test_df_SS_MCF7, y_test_df_SS_MCF7))

Mean accuracy of 2 fold cross validation:  0.9942528735632183
Best hyperparameters:  {'kernel': 'rbf'}
Best accuracy:  0.9942528735632183
Best model:  SVC(random_state=42)
Test accuracy:  0.9866666666666667

¶

4.1.2) Random Forest

Here we build the random forest classifier for the SmartSeq MCF7 dataset.

To find the best parameters, we decided to define a parameter grid and perform a grid search.

To handle overfitting and to be sure that the parameters are surely the best over the other alternstives, we decided to perform a nested cross validation.

The benefits of doing it is that Nested cross-validation with LOOCV provides an unbiased estimate of the model's performance because each sample in the dataset serves as both training and test data at some point and moreover it ensures that hyperparameter tuning is conducted independently for each training/test split, reducing the risk of overfitting to the validation set.

After that we fit the best model found by the grid search with the training dataset and we print various important information.

As we can see from the output, in this case we have a 100% accuracy and the list of the most important features provide us a good ranking of genes that the model used mostly to discriminate about the hypoxic and normoxic conditions.

In [ ]:
#i build the random forest classifier for the SmartSeq MCF7 dataset

# Define the random forest classifier
rf = RandomForestClassifier(random_state=42)

# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [10, 20, 50], 'max_depth': [10, 20, 30]}
grid_search = GridSearchCV(rf, param_grid, cv=5)

# Define the leave one out cross validation
loo = LeaveOneOut()

# Perform the nested cross validation
scores = cross_val_score(grid_search, X_train_df_SS_MCF7, y_train_df_SS_MCF7, cv=loo)

# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))

# Perform GridSearchCV on the train dataset to get the best parameters and fit the model
grid_search.fit(X_train_df_SS_MCF7, y_train_df_SS_MCF7)
print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
print("Best model: ", grid_search.best_estimator_)

#i want to test the model on the test dataset
print("Test accuracy: ", grid_search.best_estimator_.score(X_test_df_SS_MCF7, y_test_df_SS_MCF7))

# Print the feature importances
importances = grid_search.best_estimator_.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_df_SS_MCF7.shape[1]):
    print("%d. feature %s (%f)" % (f + 1, X_train_df_SS_MCF7.columns[indices[f]], importances[indices[f]]))

best_rf_SS_MCF7 = grid_search.best_estimator_

Mean accuracy of nested cross validation:  0.9885714285714285
Best hyperparameters:  {'max_depth': 10, 'n_estimators': 20}
Best accuracy:  0.9942857142857143
Best model:  RandomForestClassifier(max_depth=10, n_estimators=20, random_state=42)
Test accuracy:  1.0
Feature ranking:
1. feature "PSME2" (0.084883)
2. feature "ERO1A" (0.082939)
3. feature "ARPC1B" (0.078651)
4. feature "TUBG1" (0.074686)
5. feature "BNIP3" (0.047712)
6. feature "P4HA1" (0.044656)
7. feature "CXXC5" (0.042232)
8. feature "GPI" (0.041295)
9. feature "PFKP" (0.039222)
10. feature "KPNA2" (0.034581)
11. feature "KLF3" (0.030159)
12. feature "DOLK" (0.027947)
13. feature "SRM" (0.027246)
14. feature "FANCG" (0.024916)
15. feature "ZNF395" (0.023657)
16. feature "C1QTNF6" (0.020101)
17. feature "UBC" (0.015791)
18. feature "FTH1" (0.013772)
19. feature "CRABP2" (0.013356)
20. feature "CRELD2" (0.011669)
21. feature "EGLN3" (0.010181)
22. feature "TLE1" (0.007734)
23. feature "MT-ATP6" (0.007637)
24. feature "SLC9A3R1-AS1" (0.007464)
25. feature "NECTIN2" (0.007417)
26. feature "DYNC2I2" (0.007275)
27. feature "MYO1E" (0.007012)
28. feature "GATA3-AS1" (0.006491)
29. feature "LDHA" (0.006099)
30. feature "NR4A1" (0.005884)
31. feature "SKIL" (0.005646)
32. feature "HILPDA" (0.005478)
33. feature "NDRG1" (0.005448)
34. feature "DCXR" (0.005397)
35. feature "ABAT" (0.005395)
36. feature "DSCAM-AS1" (0.005382)
37. feature "SYTL5" (0.005376)
38. feature "PRMT6" (0.005193)
39. feature "OBSL1" (0.005032)
40. feature "BTG1" (0.004431)
41. feature "TMEM164" (0.004196)
42. feature "DDX5" (0.004188)
43. feature "HK2" (0.004176)
44. feature "SLC3A2" (0.003820)
45. feature "MT-RNR1" (0.003476)
46. feature "SPRY2" (0.003382)
47. feature "NOL3" (0.003310)
48. feature "FDFT1" (0.003169)
49. feature "SLC2A1" (0.003112)
50. feature "ITGB5" (0.003037)
51. feature "IFI30" (0.002829)
52. feature "IDH2" (0.002494)
53. feature "ANXA6" (0.002453)
54. feature "CAPG" (0.002288)
55. feature "KCNG1" (0.002270)
56. feature "ENO2" (0.002246)
57. feature "HSH2D" (0.002241)
58. feature "PDK1" (0.002231)
59. feature "MMP28" (0.002231)
60. feature "IFITM1" (0.002227)
61. feature "AACSP1" (0.002179)
62. feature "DISP2" (0.002178)
63. feature "LIMCH1" (0.002143)
64. feature "EPAS1" (0.002065)
65. feature "PDE4A" (0.001528)
66. feature "TRIM3" (0.001477)
67. feature "MTATP6P1" (0.001476)
68. feature "SFXN2" (0.001258)
69. feature "S100A13" (0.001191)
70. feature "PCLAF" (0.001166)
71. feature "SCD" (0.001147)
72. feature "VAT1L" (0.001147)
73. feature "GTSE1-DT" (0.001140)
74. feature "REEP4" (0.001134)
75. feature "LINC01016" (0.001132)
76. feature "MGAT4EP" (0.001129)
77. feature "CALML5" (0.001129)
78. feature "LPCAT1" (0.001129)
79. feature "PER1" (0.001098)
80. feature "FGF11" (0.001086)
81. feature "CENPF" (0.001031)
82. feature "NTN4" (0.001022)
83. feature "PODXL" (0.000994)
84. feature "HMGB2" (0.000953)
85. feature "FLNB" (0.000952)
86. feature "OSR2" (0.000771)
87. feature "JUNB" (0.000766)
88. feature "FERMT2" (0.000764)
89. feature "PLA2G4D" (0.000000)
90. feature "CAPN13" (0.000000)
91. feature "TMEM26" (0.000000)
92. feature "PLEKHG1" (0.000000)
93. feature "CLIP2" (0.000000)
94. feature "NR3C2" (0.000000)
95. feature "GLIS3" (0.000000)
96. feature "NHS" (0.000000)
97. feature "SPRR1B" (0.000000)
98. feature "KCND1" (0.000000)
99. feature "CNNM1" (0.000000)
100. feature "PDLIM5" (0.000000)
101. feature "COL6A1" (0.000000)
102. feature "MYT1" (0.000000)
103. feature "TMEM145" (0.000000)
104. feature "FAM13A-AS1" (0.000000)
105. feature "UMODL1" (0.000000)
106. feature "PCK1" (0.000000)
107. feature "PNPLA7" (0.000000)
108. feature "CLDN4" (0.000000)
109. feature "SPNS2" (0.000000)
110. feature "SSTR5" (0.000000)
111. feature "KRT18P59" (0.000000)
112. feature "CCDC80" (0.000000)
113. feature "HTR1D" (0.000000)
114. feature "EPHB2" (0.000000)
115. feature "TAP1" (0.000000)
116. feature "HAAO" (0.000000)
117. feature "HMMR" (0.000000)
118. feature "SASH1" (0.000000)
119. feature "DISC1" (0.000000)
120. feature "CRLF2" (0.000000)
121. feature "RASSF10" (0.000000)
122. feature "FRMPD3" (0.000000)
123. feature "STX11" (0.000000)
124. feature "SRC" (0.000000)
125. feature "NIM1K" (0.000000)
126. feature "LRRC3" (0.000000)
127. feature "ATG9B" (0.000000)
128. feature "BTBD11" (0.000000)
129. feature "PLPP2" (0.000000)
130. feature "PCNA" (0.000000)
131. feature "WEE1" (0.000000)
132. feature "TP63" (0.000000)
133. feature "GAREM1" (0.000000)
134. feature "RASGEF1C" (0.000000)
135. feature "TFPI" (0.000000)
136. feature "PHETA2" (0.000000)
137. feature "COL23A1" (0.000000)
138. feature "CABLES1" (0.000000)
139. feature "PCED1B" (0.000000)
140. feature "AKAP6" (0.000000)
141. feature "STON1" (0.000000)
142. feature "ITPR1" (0.000000)
143. feature "NCOA3" (0.000000)
144. feature "GPR68" (0.000000)
145. feature "CPNE9" (0.000000)
146. feature "NTRK2" (0.000000)
147. feature "NEURL3" (0.000000)
148. feature "NECTIN4" (0.000000)
149. feature "FAM95C" (0.000000)
150. feature "PSMD10P2" (0.000000)
151. feature "SCN5A" (0.000000)
152. feature "AKR1C3" (0.000000)
153. feature "PIK3AP1" (0.000000)
154. feature "FKBP10" (0.000000)
155. feature "ANO4" (0.000000)
156. feature "CKB" (0.000000)
157. feature "FAM83G" (0.000000)
158. feature "GAL3ST1" (0.000000)
159. feature "EPHA2" (0.000000)
160. feature "IRF8" (0.000000)
161. feature "SLC12A7" (0.000000)
162. feature "TUBA8" (0.000000)
163. feature "TGFB2" (0.000000)
164. feature "FBXO24" (0.000000)
165. feature "LOXL1" (0.000000)
166. feature "TCIRG1" (0.000000)
167. feature "GPR37" (0.000000)
168. feature "TMEM238L" (0.000000)
169. feature "CGB7" (0.000000)
170. feature "PRKCG" (0.000000)
171. feature "PATL2" (0.000000)
172. feature "LRG1" (0.000000)
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2690. feature "NUP188" (0.000000)
2691. feature "CARD10" (0.000000)
2692. feature "GPX3" (0.000000)
2693. feature "ZSWIM3" (0.000000)
2694. feature "KIAA0319" (0.000000)
2695. feature "SPRY1" (0.000000)
2696. feature "CAPN10-DT" (0.000000)
2697. feature "TEX19" (0.000000)
2698. feature "GHDC" (0.000000)
2699. feature "COL24A1" (0.000000)
2700. feature "GRIK2" (0.000000)
2701. feature "CCDC63" (0.000000)
2702. feature "NAPSA" (0.000000)
2703. feature "PASK" (0.000000)
2704. feature "CLCN4" (0.000000)
2705. feature "KRT223P" (0.000000)
2706. feature "EDA" (0.000000)
2707. feature "BFSP2" (0.000000)
2708. feature "COL6A4P1" (0.000000)
2709. feature "ZNF589" (0.000000)
2710. feature "PSG4" (0.000000)
2711. feature "OXER1" (0.000000)
2712. feature "IFNLR1" (0.000000)
2713. feature "KIF1A" (0.000000)
2714. feature "BCAN-AS1" (0.000000)
2715. feature "LRRC2" (0.000000)
2716. feature "CA8" (0.000000)
2717. feature "SNRNP25" (0.000000)
2718. feature "FZR1" (0.000000)
2719. feature "DGAT2" (0.000000)
2720. feature "STC1" (0.000000)
2721. feature "FRMD4B" (0.000000)
2722. feature "SGSM2" (0.000000)
2723. feature "FAM234B" (0.000000)
2724. feature "PABPC4L" (0.000000)
2725. feature "FRK" (0.000000)
2726. feature "LRIG3" (0.000000)
2727. feature "LINC00963" (0.000000)
2728. feature "MYO15A" (0.000000)
2729. feature "ZNF304" (0.000000)
2730. feature "NTSR2" (0.000000)
2731. feature "CKAP5" (0.000000)
2732. feature "LINC01869" (0.000000)
2733. feature "ZBTB32" (0.000000)
2734. feature "KCNQ1" (0.000000)
2735. feature "LINC00242" (0.000000)
2736. feature "SLC45A1" (0.000000)
2737. feature "ABCD1" (0.000000)
2738. feature "COL7A1" (0.000000)
2739. feature "S100A11" (0.000000)
2740. feature "ZNF423" (0.000000)
2741. feature "TMIE" (0.000000)
2742. feature "TPPP" (0.000000)
2743. feature "SLC12A2" (0.000000)
2744. feature "SCART1" (0.000000)
2745. feature "NTNG1" (0.000000)
2746. feature "HEY2" (0.000000)
2747. feature "NOL6" (0.000000)
2748. feature "CDC42EP1" (0.000000)
2749. feature "LINC01426" (0.000000)
2750. feature "SLC4A9" (0.000000)
2751. feature "ANKRD2" (0.000000)
2752. feature "ASPHD2" (0.000000)
2753. feature "TMSB4X" (0.000000)
2754. feature "CDC25C" (0.000000)
2755. feature "KHK" (0.000000)
2756. feature "IL17C" (0.000000)
2757. feature "RAB37" (0.000000)
2758. feature "LAMA3" (0.000000)
2759. feature "FBLN7" (0.000000)
2760. feature "ANKRD62P1" (0.000000)
2761. feature "FBXO32" (0.000000)
2762. feature "GNMT" (0.000000)
2763. feature "ZFYVE1" (0.000000)
2764. feature "ADPRH" (0.000000)
2765. feature "NRARP" (0.000000)
2766. feature "OR2AF1P" (0.000000)
2767. feature "LDHD" (0.000000)
2768. feature "TRIM54" (0.000000)
2769. feature "MMP16" (0.000000)
2770. feature "CHKA-DT" (0.000000)
2771. feature "IGFBPL1" (0.000000)
2772. feature "PEX5" (0.000000)
2773. feature "CST8" (0.000000)
2774. feature "KRTAP5-AS1" (0.000000)
2775. feature "GAD1" (0.000000)
2776. feature "FAM3B" (0.000000)
2777. feature "MAP3K1" (0.000000)
2778. feature "NQO1" (0.000000)
2779. feature "HLA-B" (0.000000)
2780. feature "ADGRF2" (0.000000)
2781. feature "IGSF8" (0.000000)
2782. feature "RGS3" (0.000000)
2783. feature "MST1" (0.000000)
2784. feature "RPS6KA2" (0.000000)
2785. feature "FAM102A" (0.000000)
2786. feature "ADCY6" (0.000000)
2787. feature "SYTL4" (0.000000)
2788. feature "ACRV1" (0.000000)
2789. feature "SYNPO" (0.000000)
2790. feature "HTR1E" (0.000000)
2791. feature "HSD17B14" (0.000000)
2792. feature "CSF1R" (0.000000)
2793. feature "GPR179" (0.000000)
2794. feature "FAIM2" (0.000000)
2795. feature "PIEZO2" (0.000000)
2796. feature "RFC2" (0.000000)
2797. feature "VPS18" (0.000000)
2798. feature "RAB7B" (0.000000)
2799. feature "DOK6" (0.000000)
2800. feature "HACD4" (0.000000)
2801. feature "MDGA2" (0.000000)
2802. feature "TRIM47" (0.000000)
2803. feature "SOAT2" (0.000000)
2804. feature "SYTL3" (0.000000)
2805. feature "AGTR1" (0.000000)
2806. feature "TTC9" (0.000000)
2807. feature "FGD3" (0.000000)
2808. feature "SIGMAR1" (0.000000)
2809. feature "EPS8L3" (0.000000)
2810. feature "BACE2" (0.000000)
2811. feature "OTUD7A" (0.000000)
2812. feature "TRBV30" (0.000000)
2813. feature "PLEKHB1" (0.000000)
2814. feature "PPEF1" (0.000000)
2815. feature "PDE10A" (0.000000)
2816. feature "RNF19B" (0.000000)
2817. feature "LRRK1" (0.000000)
2818. feature "FGR" (0.000000)
2819. feature "ERN1" (0.000000)
2820. feature "PIDD1" (0.000000)
2821. feature "VLDLR" (0.000000)
2822. feature "C3orf70" (0.000000)
2823. feature "IER2" (0.000000)
2824. feature "PITPNM3" (0.000000)
2825. feature "BLOC1S5-TXNDC5" (0.000000)
2826. feature "ITGA8" (0.000000)
2827. feature "MSS51" (0.000000)
2828. feature "SMTNL2" (0.000000)
2829. feature "JADE1" (0.000000)
2830. feature "ZIC3" (0.000000)
2831. feature "CEBPG" (0.000000)
2832. feature "SERPINA1" (0.000000)
2833. feature "TOB1" (0.000000)
2834. feature "VSIG1" (0.000000)
2835. feature "DLK2" (0.000000)
2836. feature "TH" (0.000000)
2837. feature "C1QTNF12" (0.000000)
2838. feature "ACE2" (0.000000)
2839. feature "FAM110D" (0.000000)
2840. feature "PHF2P2" (0.000000)
2841. feature "SH2D5" (0.000000)
2842. feature "LARGE2" (0.000000)
2843. feature "WSB1" (0.000000)
2844. feature "TICRR" (0.000000)
2845. feature "CLK3" (0.000000)
2846. feature "CCNE1" (0.000000)
2847. feature "NCALD" (0.000000)
2848. feature "PTPRR" (0.000000)
2849. feature "HSPG2" (0.000000)
2850. feature "MCIDAS" (0.000000)
2851. feature "SLCO5A1" (0.000000)
2852. feature "DNMT1" (0.000000)
2853. feature "BOP1" (0.000000)
2854. feature "DNAH3" (0.000000)
2855. feature "OR7E22P" (0.000000)
2856. feature "SLC29A3" (0.000000)
2857. feature "GPR87" (0.000000)
2858. feature "TRIB1" (0.000000)
2859. feature "FRAS1" (0.000000)
2860. feature "DSC1" (0.000000)
2861. feature "PHF19" (0.000000)
2862. feature "IKBKE" (0.000000)
2863. feature "PKDCC" (0.000000)
2864. feature "LINC01686" (0.000000)
2865. feature "ELFN2" (0.000000)
2866. feature "LINC02878" (0.000000)
2867. feature "CELSR3" (0.000000)
2868. feature "USP27X-DT" (0.000000)
2869. feature "PLXNA1" (0.000000)
2870. feature "MB" (0.000000)
2871. feature "SORBS1" (0.000000)
2872. feature "RAB36" (0.000000)
2873. feature "TECTA" (0.000000)
2874. feature "PTPRU" (0.000000)
2875. feature "L3MBTL1" (0.000000)
2876. feature "CAMK1D" (0.000000)
2877. feature "C1orf127" (0.000000)
2878. feature "SLC9A7" (0.000000)
2879. feature "SPTBN4" (0.000000)
2880. feature "CACNA1I" (0.000000)
2881. feature "SCUBE3" (0.000000)
2882. feature "SLC9A7P1" (0.000000)
2883. feature "DMP1" (0.000000)
2884. feature "EXO1" (0.000000)
2885. feature "MAGED1" (0.000000)
2886. feature "EML2-AS1" (0.000000)
2887. feature "PINK1" (0.000000)
2888. feature "IFIT2" (0.000000)
2889. feature "PCDHGB8P" (0.000000)
2890. feature "STOX2" (0.000000)
2891. feature "EHD3" (0.000000)
2892. feature "SDR16C5" (0.000000)
2893. feature "LINC01185" (0.000000)
2894. feature "SYT16" (0.000000)
2895. feature "GUCY1A2" (0.000000)
2896. feature "PTK2B" (0.000000)
2897. feature "KIF12" (0.000000)
2898. feature "SV2B" (0.000000)
2899. feature "MT-ATP8" (0.000000)
2900. feature "INKA2-AS1" (0.000000)
2901. feature "CDC25A" (0.000000)
2902. feature "IPO4" (0.000000)
2903. feature "KCNK6" (0.000000)
2904. feature "SLC1A2" (0.000000)
2905. feature "E2F2" (0.000000)
2906. feature "NR2F2" (0.000000)
2907. feature "OSBPL7" (0.000000)
2908. feature "GIPR" (0.000000)
2909. feature "RIN1" (0.000000)
2910. feature "IGF1R" (0.000000)
2911. feature "ABCC10" (0.000000)
2912. feature "BVES" (0.000000)
2913. feature "ZBTB42" (0.000000)
2914. feature "FHOD3" (0.000000)
2915. feature "FAM171A1" (0.000000)
2916. feature "SOX9-AS1" (0.000000)
2917. feature "PLOD2" (0.000000)
2918. feature "ZNF343" (0.000000)
2919. feature "ST6GALNAC5" (0.000000)
2920. feature "TTC28" (0.000000)
2921. feature "TRPM6" (0.000000)
2922. feature "DISP1" (0.000000)
2923. feature "ADAMTS13" (0.000000)
2924. feature "RASIP1" (0.000000)
2925. feature "ODAPH" (0.000000)
2926. feature "ENO1P3" (0.000000)
2927. feature "ELAVL2" (0.000000)
2928. feature "SLC25A39" (0.000000)
2929. feature "IL27RA" (0.000000)
2930. feature "PPP1R14D" (0.000000)
2931. feature "TYRO3" (0.000000)
2932. feature "EVPLL" (0.000000)
2933. feature "ZSCAN18" (0.000000)
2934. feature "LGALS9" (0.000000)
2935. feature "MELK" (0.000000)
2936. feature "ATP6V1FNB" (0.000000)
2937. feature "ARMCX2" (0.000000)
2938. feature "ZNF827" (0.000000)
2939. feature "ACRBP" (0.000000)
2940. feature "DGKQ" (0.000000)
2941. feature "ARHGEF16" (0.000000)
2942. feature "KIF18B" (0.000000)
2943. feature "PAH" (0.000000)
2944. feature "SALL2" (0.000000)
2945. feature "NRP2" (0.000000)
2946. feature "MAFB" (0.000000)
2947. feature "BSCL2" (0.000000)
2948. feature "HS3ST1" (0.000000)
2949. feature "HES1" (0.000000)
2950. feature "H1-0" (0.000000)
2951. feature "HLA-DPA1" (0.000000)
2952. feature "ALDH1L2" (0.000000)
2953. feature "ZNF70" (0.000000)
2954. feature "KRT40" (0.000000)
2955. feature "FNDC5" (0.000000)
2956. feature "ANO7" (0.000000)
2957. feature "EYA4" (0.000000)
2958. feature "LINC01260" (0.000000)
2959. feature "TYK2" (0.000000)
2960. feature "SLC22A23" (0.000000)
2961. feature "KCNK15" (0.000000)
2962. feature "WNT6" (0.000000)
2963. feature "A4GALT" (0.000000)
2964. feature "ERG" (0.000000)
2965. feature "CTTNBP2" (0.000000)
2966. feature "KCNJ13" (0.000000)
2967. feature "GBP2" (0.000000)
2968. feature "ZC2HC1C" (0.000000)
2969. feature "LINC01119" (0.000000)
2970. feature "SNTB1" (0.000000)
2971. feature "LPCAT2" (0.000000)
2972. feature "H2AZ1" (0.000000)
2973. feature "AHR" (0.000000)
2974. feature "HLA-DRB6" (0.000000)
2975. feature "AMER1" (0.000000)
2976. feature "RASL10B" (0.000000)
2977. feature "SNTA1" (0.000000)
2978. feature "GPR199P" (0.000000)
2979. feature "RAET1L" (0.000000)
2980. feature "LINC00324" (0.000000)
2981. feature "POU2F2" (0.000000)
2982. feature "GPX2" (0.000000)
2983. feature "ZNF543" (0.000000)
2984. feature "SULT2B1" (0.000000)
2985. feature "CCR6" (0.000000)
2986. feature "KIF14" (0.000000)
2987. feature "LTBP1" (0.000000)
2988. feature "SH3BP5" (0.000000)
2989. feature "CYBA" (0.000000)
2990. feature "RMI2" (0.000000)
2991. feature "HGFAC" (0.000000)
2992. feature "COLQ" (0.000000)
2993. feature "HYAL2" (0.000000)
2994. feature "LINC02533" (0.000000)
2995. feature "CFB" (0.000000)
2996. feature "PPM1M" (0.000000)
2997. feature "AATBC" (0.000000)
2998. feature "SLC18B1" (0.000000)
2999. feature "LINC02532" (0.000000)
3000. feature "CYP1B1" (0.000000)

¶

4.1.3) XGBoost is not necessary since Test accuracy is already 1.0

¶

4.1.4) KNN

In [ ]:
best_knn_SS_MCF7=train_and_evaluate_knn(X_train_SS_MCF, X_test_SS_MCF, y_train_SS_MCF, y_test_SS_MCF)

Best hyperparameters:  {'n_neighbors': 3}
Accuracy: 1.0
F1: 1.0
Classification Report:
              precision    recall  f1-score   support

         0.0       1.00      1.00      1.00        25
         1.0       1.00      1.00      1.00        25

    accuracy                           1.00        50
   macro avg       1.00      1.00      1.00        50
weighted avg       1.00      1.00      1.00        50

¶

4.1.5) Logistic Regression

In [ ]:
best_logreg_SS_MCF7=train_and_evaluate_logreg(X_train_SS_MCF, X_test_SS_MCF, y_train_SS_MCF, y_test_SS_MCF)

Best hyperparameters:  {'C': 0.002, 'penalty': 'l2'}
Accuracy: 1.0
F1: 1.0
Classification Report:
              precision    recall  f1-score   support

         0.0       1.00      1.00      1.00        25
         1.0       1.00      1.00      1.00        25

    accuracy                           1.00        50
   macro avg       1.00      1.00      1.00        50
weighted avg       1.00      1.00      1.00        50
In [ ]:
X_train_pca_SS_MCF, X_test_pca_SS_MCF, y_train_SS_MCF, y_test_SS_MCF,scaler,pca_SS_MCF = prepare_data_pca(df_trans_SS_MCF)
In [ ]:
model_logreg_mc_SMART_ver2_SS_MCF=train_and_evaluate_logreg(X_train_pca_SS_MCF, X_test_pca_SS_MCF, y_train_SS_MCF, y_test_SS_MCF)

Best hyperparameters:  {'C': 0.002, 'penalty': 'l2'}
Accuracy: 1.0
F1: 1.0
Classification Report:
              precision    recall  f1-score   support

         0.0       1.00      1.00      1.00        25
         1.0       1.00      1.00      1.00        25

    accuracy                           1.00        50
   macro avg       1.00      1.00      1.00        50
weighted avg       1.00      1.00      1.00        50

¶

4.1.6) CatBoost

In [ ]:
best_catboost_SS_MCF7=train_and_evaluate_catboost(X_train_SS_MCF, X_test_SS_MCF, y_train_SS_MCF, y_test_SS_MCF)

Best hyperparameters:  {'logging_level': 'Silent'}
Accuracy: 1.0
F1: 1.0
Classification Report:
              precision    recall  f1-score   support

         0.0       1.00      1.00      1.00        25
         1.0       1.00      1.00      1.00        25

    accuracy                           1.00        50
   macro avg       1.00      1.00      1.00        50
weighted avg       1.00      1.00      1.00        50
In [ ]:
new_columns_names_SS_MCF = []
for i in range(1,3001):
    name_new_SS_MCF = f'f{i}'
    new_columns_names_SS_MCF.append(name_new_SS_MCF)
X_test_pd_SS_MCF=pd.DataFrame(X_test_SS_MCF, columns=new_columns_names_SS_MCF)
X_train_pd_SS_MCF=pd.DataFrame(X_train_SS_MCF, columns=new_columns_names_SS_MCF)
In [ ]:
model_catboost_mc_SMART_ver2_SS_MCF, cols_cut_SS_MCF=train_and_evaluate_catboost2(X_train_pd_SS_MCF, X_test_pd_SS_MCF, y_train_SS_MCF, y_test_SS_MCF)

1304
Best hyperparameters:  {'l2_leaf_reg': 3, 'logging_level': 'Silent', 'n_estimators': 500}
Accuracy: 1.0
F1: 1.0
Classification Report:
              precision    recall  f1-score   support

         0.0       1.00      1.00      1.00        25
         1.0       1.00      1.00      1.00        25

    accuracy                           1.00        50
   macro avg       1.00      1.00      1.00        50
weighted avg       1.00      1.00      1.00        50

¶

4.2) PREDICTORS SMARTSEQ HCC1806

We apply the same steps of the dataset before (see 4.1) to the SmartSeq HCC1806 dataset

In [ ]:
#load the SmartSeq HCC1806 dataset
df_SS_HCC1806_norm_filt = pd.read_csv("SmartSeq\HCC1806_SmartS_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ",engine='python',index_col=0)

df_SS_HCC1806_norm_filtT = df_SS_HCC1806_norm_filt.T

# Add the target column
df_SS_HCC1806_norm_filtT['target'] = np.where(df_SS_HCC1806_norm_filtT.index.str.contains('Hypo'), 1, 0)

#i want to split the data into training and testing data, with a balance of 70% training and 30% testing

X_SS_HCC1806 = df_SS_HCC1806_norm_filtT.drop('target', axis=1)
y_SS_HCC1806 = df_SS_HCC1806_norm_filtT['target']

X_train_df_SS_HCC1806, X_test_df_SS_HCC1806, y_train_df_SS_HCC1806, y_test_df_SS_HCC1806 = train_test_split(X_SS_HCC1806, y_SS_HCC1806, test_size=0.3, random_state=42)
In [ ]:
df_basic_SS_HCC=df_SS_HCC1806_norm_filt
df_trans_SS_HCC=df_basic_SS_HCC.T
df_trans_SS_HCC['TARGET'] = df_trans_SS_HCC.columns.to_series().apply(lambda x: 1 if 'Hypo' in x else 0)
df_trans_SS_HCC.loc[df_trans_SS_HCC.index.str.contains('Norm'), 'TARGET'] = 0
df_trans_SS_HCC.loc[df_trans_SS_HCC.index.str.contains('Hypo'), 'TARGET'] = 1
In [ ]:
X_train_SS_HCC, X_test_SS_HCC, y_train_SS_HCC, y_test_SS_HCC,scaler_SS_HCC = prepare_data(df_trans_SS_HCC)

¶

4.2.1) SVM

We used same approach used for SmartSeq MCF7, see 4.1.1.

In [ ]:
# build SVM on SmartSeq HCC1806

# Define the SVM classifier
svm = SVC(random_state=42)

# Define the parameter grid for GridSearchCV
param_grid = {'kernel': ['linear', 'rbf']}
grid_search_svm = GridSearchCV(svm, param_grid, cv=5)

# Define the leave one out cross validation
#loo = LeaveOneOut()

# Perform the nested cross validation
scores_svm = cross_val_score(grid_search_svm, X_train_df_SS_HCC1806, y_train_df_SS_HCC1806, cv=5)

# Print the mean accuracy of the nested cross validation
print("Mean accuracy of 5 fold nested cross validation: ", np.mean(scores_svm))

# Perform GridSearchCV on the train dataset to get the best parameters and fit the model
grid_search_svm.fit(X_train_df_SS_HCC1806, y_train_df_SS_HCC1806)
print("Best kernel: ", grid_search_svm.best_params_)
print("Best accuracy: ", grid_search_svm.best_score_)
best_svm_SS_HCC1806 = grid_search_svm.best_estimator_
print("Best model: ", best_svm_SS_HCC1806)

# Test the model on the test dataset
print("Test accuracy: ", best_svm_SS_HCC1806.score(X_test_df_SS_HCC1806, y_test_df_SS_HCC1806))

Mean accuracy of 5 fold nested cross validation:  0.9538461538461538
Best kernel:  {'kernel': 'linear'}
Best accuracy:  0.9846153846153847
Best model:  SVC(kernel='linear', random_state=42)
Test accuracy:  1.0

¶

4.2.2) Random Forest

Here we build the random forest classifier for the SmartSeq HCC1806 dataset following exactly the same structure as before.

We notice that in this case the performance is a little lower with respect to the other one as this estimator has a perfomance approximately of 98.18%.

In [ ]:
#i build the random forest classifier for the SmartSeq HCC1806 dataset

# Define the random forest classifier
rf = RandomForestClassifier(random_state=42)

# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [10, 20, 50], 'max_depth': [10, 20, 30]}
grid_search = GridSearchCV(rf, param_grid, cv=5)

# Define the leave one out cross validation
loo = LeaveOneOut()

# Perform the nested cross validation
scores = cross_val_score(grid_search, X_train_df_SS_HCC1806, y_train_df_SS_HCC1806, cv=loo)

# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))

# Perform GridSearchCV on the train dataset to get the best parameters and fit the model
grid_search.fit(X_train_df_SS_HCC1806, y_train_df_SS_HCC1806)
print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
print("Best model: ", grid_search.best_estimator_)

#i want to test the model on the test dataset
print("Test accuracy: ", grid_search.best_estimator_.score(X_test_df_SS_HCC1806, y_test_df_SS_HCC1806))

# Print the feature importances
importances = grid_search.best_estimator_.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_df_SS_HCC1806.shape[1]):
    print("%d. feature %s (%f)" % (f + 1, X_train_df_SS_HCC1806.columns[indices[f]], importances[indices[f]]))

best_rf_SS_HCC1806 = grid_search.best_estimator_

Mean accuracy of nested cross validation:  0.984251968503937
Best hyperparameters:  {'max_depth': 10, 'n_estimators': 50}
Best accuracy:  0.9846153846153847
Best model:  RandomForestClassifier(max_depth=10, n_estimators=50, random_state=42)
Test accuracy:  0.9818181818181818
Feature ranking:
1. feature "BNIP3" (0.051024)
2. feature "NDRG1" (0.049045)
3. feature "FAM162A" (0.042764)
4. feature "BNIP3L" (0.029959)
5. feature "LOXL2" (0.026526)
6. feature "CA9" (0.024664)
7. feature "ANGPTL4" (0.021798)
8. feature "BLCAP" (0.021322)
9. feature "PFKFB3" (0.020018)
10. feature "DDIT4" (0.019817)
11. feature "ALDOA" (0.018799)
12. feature "STC2" (0.018340)
13. feature "LDHA" (0.017305)
14. feature "ERO1A" (0.017265)
15. feature "PLOD2" (0.017054)
16. feature "ALDOC" (0.016458)
17. feature "BHLHE40" (0.016411)
18. feature "HMGCR" (0.016001)
19. feature "SLC2A1" (0.015453)
20. feature "INSIG1" (0.014220)
21. feature "PLAC8" (0.014200)
22. feature "MIF" (0.014096)
23. feature "ENO2" (0.013592)
24. feature "EGLN1" (0.013477)
25. feature "PLIN2" (0.011913)
26. feature "PPP1R3G" (0.011837)
27. feature "EIF5" (0.011658)
28. feature "EGLN3" (0.011127)
29. feature "SNX33" (0.010708)
30. feature "P4HA1" (0.009833)
31. feature "H1-0" (0.009441)
32. feature "FSCN1" (0.009288)
33. feature "GPI" (0.009261)
34. feature "PGK1" (0.008304)
35. feature "HES1" (0.008192)
36. feature "SLC38A2" (0.007999)
37. feature "LBH" (0.006475)
38. feature "MIF-AS1" (0.006367)
39. feature "COL6A1" (0.006199)
40. feature "AREG" (0.006158)
41. feature "MOB3A" (0.006005)
42. feature "UNC13D" (0.005927)
43. feature "MUC20-OT1" (0.005553)
44. feature "C4orf3" (0.005547)
45. feature "NEDD9" (0.005483)
46. feature "FUT11" (0.005374)
47. feature "DKK1" (0.005246)
48. feature "DHCR24" (0.004993)
49. feature "IER2" (0.004860)
50. feature "GYS1" (0.004837)
51. feature "L1CAM" (0.004722)
52. feature "NQO1" (0.004721)
53. feature "FAM13A" (0.004630)
54. feature "HK2" (0.004443)
55. feature "GRN" (0.004023)
56. feature "PFKFB4" (0.003864)
57. feature "ZWINT" (0.003773)
58. feature "DEGS2" (0.003706)
59. feature "FXYD3" (0.003678)
60. feature "PSMD2" (0.003654)
61. feature "DHRS3" (0.003509)
62. feature "SRXN1" (0.003453)
63. feature "CD69" (0.003432)
64. feature "LAMP3" (0.003361)
65. feature "DUSP1" (0.003243)
66. feature "KDM5B" (0.003218)
67. feature "CDH1" (0.003207)
68. feature "TMEM45A" (0.003073)
69. feature "TTK" (0.003028)
70. feature "CCN1" (0.003022)
71. feature "CDK2AP2" (0.003005)
72. feature "HBP1" (0.002978)
73. feature "ZDHHC23" (0.002874)
74. feature "CALHM2" (0.002822)
75. feature "MT-CYB" (0.002776)
76. feature "KYNU" (0.002742)
77. feature "NOL6" (0.002672)
78. feature "AHNAK" (0.002667)
79. feature "FOSL2" (0.002662)
80. feature "IGFBP3" (0.002506)
81. feature "HERPUD1" (0.002472)
82. feature "CSTB" (0.002384)
83. feature "RPLP0P2" (0.002362)
84. feature "EXPH5" (0.002324)
85. feature "UBE2C" (0.002324)
86. feature "CKS2" (0.002256)
87. feature "ITGB5" (0.002255)
88. feature "KCTD11" (0.002243)
89. feature "BTN2A1" (0.002181)
90. feature "MT-ND4" (0.002156)
91. feature "SFN" (0.002087)
92. feature "PAM" (0.001851)
93. feature "TTC28" (0.001836)
94. feature "C10orf55" (0.001825)
95. feature "RPL10P12" (0.001824)
96. feature "LINC02614" (0.001801)
97. feature "AKR1B1" (0.001800)
98. feature "UGDH" (0.001760)
99. feature "PHLDA1-AS1" (0.001757)
100. feature "IFIT2" (0.001731)
101. feature "KNSTRN" (0.001713)
102. feature "UBE2S" (0.001709)
103. feature "DLSTP1" (0.001706)
104. feature "PAIP1P1" (0.001704)
105. feature "PLK3" (0.001702)
106. feature "AGR2" (0.001689)
107. feature "PLK2" (0.001664)
108. feature "NLRP10" (0.001625)
109. feature "HSPB8" (0.001621)
110. feature "ESPL1" (0.001565)
111. feature "LEAP2" (0.001532)
112. feature "SLCO4A1" (0.001519)
113. feature "NFKBIZ" (0.001502)
114. feature "RIMKLA" (0.001456)
115. feature "HCP5" (0.001374)
116. feature "AZIN2" (0.001360)
117. feature "GLCCI1" (0.001357)
118. feature "LTBP2" (0.001328)
119. feature "TXNRD1" (0.001293)
120. feature "GALNT5" (0.001282)
121. feature "DOCK10" (0.001261)
122. feature "PHF19" (0.001228)
123. feature "NOLC1" (0.001224)
124. feature "H4C3" (0.001218)
125. feature "CPLANE2" (0.001218)
126. feature "TSC22D1" (0.001217)
127. feature "KIF22" (0.001214)
128. feature "ZNF433" (0.001208)
129. feature "BEST1" (0.001193)
130. feature "UGT1A10" (0.001190)
131. feature "IL1A" (0.001182)
132. feature "CRY2" (0.001181)
133. feature "AVPI1" (0.001177)
134. feature "NUDT11" (0.001174)
135. feature "NUCB2" (0.001174)
136. feature "SPRYD3" (0.001172)
137. feature "ADM" (0.001167)
138. feature "JDP2-AS1" (0.001159)
139. feature "PYGB" (0.001156)
140. feature "PRSS23" (0.001143)
141. feature "TMPRSS13" (0.001124)
142. feature "ASF1B" (0.001121)
143. feature "FSIP2" (0.001100)
144. feature "ADGRG2" (0.001090)
145. feature "HMGA1" (0.001088)
146. feature "FBXO2" (0.001080)
147. feature "PPP2R2B" (0.001067)
148. feature "BBS12" (0.001066)
149. feature "TACSTD2" (0.001045)
150. feature "BCL11B" (0.001042)
151. feature "HTR7" (0.001002)
152. feature "ID3" (0.000985)
153. feature "ENTPD3" (0.000982)
154. feature "MYBL2" (0.000974)
155. feature "RND3" (0.000958)
156. feature "PRKG2" (0.000950)
157. feature "PKP1" (0.000945)
158. feature "ZBTB3" (0.000907)
159. feature "JUP" (0.000907)
160. feature "MT1X" (0.000828)
161. feature "KDM6A" (0.000817)
162. feature "MAL2" (0.000787)
163. feature "CCDC87" (0.000756)
164. feature "CUL7" (0.000663)
165. feature "ENPP5" (0.000648)
166. feature "RNASE7" (0.000642)
167. feature "NUPR1" (0.000629)
168. feature "PTP4A1" (0.000629)
169. feature "CNIH3" (0.000628)
170. feature "ACTG1P10" (0.000628)
171. feature "SCARA3" (0.000626)
172. feature "INSYN2B" (0.000625)
173. feature "HTR2B" (0.000624)
174. feature "KCTD8" (0.000623)
175. feature "TMSB15A" (0.000622)
176. feature "FGFBP1" (0.000621)
177. feature "PCDH10" (0.000621)
178. feature "MCM10" (0.000621)
179. feature "ZNF182" (0.000621)
180. feature "CLIC2" (0.000621)
181. feature "CCNB1" (0.000620)
182. feature "SCD5" (0.000619)
183. feature "KRTAP2-3" (0.000619)
184. feature "APOE" (0.000619)
185. feature "S100A16" (0.000619)
186. feature "MOCS1" (0.000619)
187. feature "MAPK8IP2" (0.000619)
188. feature "ELMO3" (0.000619)
189. feature "KLK8" (0.000619)
190. feature "TP53INP1" (0.000619)
191. feature "ZNF227" (0.000619)
192. feature "VWA5A" (0.000618)
193. feature "CLIC3" (0.000618)
194. feature "ARRDC3" (0.000618)
195. feature "CTSE" (0.000617)
196. feature "NRG1" (0.000616)
197. feature "TBX5" (0.000616)
198. feature "EIF2AK3-DT" (0.000616)
199. feature "KLF6" (0.000615)
200. feature "SERPINE1" (0.000614)
201. feature "MT-CO3" (0.000614)
202. feature "CCNB3" (0.000613)
203. feature "ABCA3" (0.000613)
204. feature "DGKI" (0.000612)
205. feature "CRKL" (0.000611)
206. feature "ASS1P12" (0.000609)
207. feature "CAPN5" (0.000608)
208. feature "TRAM1L1" (0.000606)
209. feature "REEP1" (0.000602)
210. feature "KRT5" (0.000601)
211. feature "TAGLN" (0.000598)
212. feature "TNFRSF21" (0.000598)
213. feature "CD99L2" (0.000597)
214. feature "OLR1" (0.000597)
215. feature "SHCBP1" (0.000597)
216. feature "TMEM45B" (0.000596)
217. feature "ADIRF-AS1" (0.000595)
218. feature "PLAUR" (0.000590)
219. feature "TATDN2P2" (0.000586)
220. feature "FHL1" (0.000586)
221. feature "PCNA" (0.000583)
222. feature "CPEB2" (0.000578)
223. feature "MRNIP" (0.000577)
224. feature "SLC7A11" (0.000571)
225. feature "ABCA1" (0.000567)
226. feature "SLC43A3" (0.000567)
227. feature "SERPINI1" (0.000564)
228. feature "IGFL2-AS1" (0.000564)
229. feature "SLC7A2" (0.000561)
230. feature "ARC" (0.000554)
231. feature "MOV10L1" (0.000539)
232. feature "MIR34AHG" (0.000525)
233. feature "LARP4P" (0.000525)
234. feature "SERPINB11" (0.000509)
235. feature "GNMT" (0.000508)
236. feature "ITGA2" (0.000505)
237. feature "AQP3" (0.000504)
238. feature "TSNARE1" (0.000504)
239. feature "ZNF470" (0.000504)
240. feature "B3GNT3" (0.000488)
241. feature "LIG1" (0.000477)
242. feature "H4C8" (0.000473)
243. feature "FOS" (0.000472)
244. feature "FOXD2" (0.000472)
245. feature "SYNGR3" (0.000424)
246. feature "NDC80" (0.000423)
247. feature "CENPF" (0.000422)
248. feature "ENPP2" (0.000422)
249. feature "ISL2" (0.000420)
250. feature "CALB1" (0.000321)
251. feature "DEPTOR" (0.000316)
252. feature "HES5" (0.000315)
253. feature "PTGES" (0.000306)
254. feature "HPGD" (0.000299)
255. feature "SH3TC2-DT" (0.000299)
256. feature "POLD1" (0.000195)
257. feature "ZNF853" (0.000000)
258. feature "IFIT1" (0.000000)
259. feature "PELI2" (0.000000)
260. feature "KRT13" (0.000000)
261. feature "ZSWIM3" (0.000000)
262. feature "IQGAP2" (0.000000)
263. feature "CAND2" (0.000000)
264. feature "KRT7" (0.000000)
265. feature "NPR3" (0.000000)
266. feature "MC1R" (0.000000)
267. feature "SMIM5" (0.000000)
268. feature "GDPD3" (0.000000)
269. feature "ERICH5" (0.000000)
270. feature "DGAT2" (0.000000)
271. feature "ABAT" (0.000000)
272. feature "LINC00161" (0.000000)
273. feature "SIGLEC15" (0.000000)
274. feature "EDIL3" (0.000000)
275. feature "AURKC" (0.000000)
276. feature "PACSIN1" (0.000000)
277. feature "ZFP36L1" (0.000000)
278. feature "MVP" (0.000000)
279. feature "PPP4R4" (0.000000)
280. feature "CDC45" (0.000000)
281. feature "EPHA10" (0.000000)
282. feature "UNC13B" (0.000000)
283. feature "MAG" (0.000000)
284. feature "TCHH" (0.000000)
285. feature "DNAJB5" (0.000000)
286. feature "PPP1R36" (0.000000)
287. feature "THSD7A" (0.000000)
288. feature "AGMO" (0.000000)
289. feature "ZBTB26" (0.000000)
290. feature "CXCL2" (0.000000)
291. feature "KCNIP3" (0.000000)
292. feature "THOC1-DT" (0.000000)
293. feature "EFEMP1" (0.000000)
294. feature "ZNF425" (0.000000)
295. feature "RASL10A" (0.000000)
296. feature "SESN2" (0.000000)
297. feature "VAMP1" (0.000000)
298. feature "ANO9" (0.000000)
299. feature "IL17RB" (0.000000)
300. feature "KRT17" (0.000000)
301. feature "CMYA5" (0.000000)
302. feature "CYP26C1" (0.000000)
303. feature "STAT4" (0.000000)
304. feature "PADI1" (0.000000)
305. feature "SOWAHA" (0.000000)
306. feature "GLRA3" (0.000000)
307. feature "PLA2G4D" (0.000000)
308. feature "ARRDC4" (0.000000)
309. feature "MSMO1" (0.000000)
310. feature "TUBA8" (0.000000)
311. feature "MMP1" (0.000000)
312. feature "GLUL" (0.000000)
313. feature "MUC1" (0.000000)
314. feature "MMP13" (0.000000)
315. feature "SLC30A3" (0.000000)
316. feature "TFRC" (0.000000)
317. feature "IGFBP1" (0.000000)
318. feature "GCSAM" (0.000000)
319. feature "ADCY6" (0.000000)
320. feature "LRRC2" (0.000000)
321. feature "SNCG" (0.000000)
322. feature "ZNF311" (0.000000)
323. feature "TMEM187" (0.000000)
324. feature "PDE2A" (0.000000)
325. feature "STOX1" (0.000000)
326. feature "LINC00235" (0.000000)
327. feature "DGCR5" (0.000000)
328. feature "CPE" (0.000000)
329. feature "PLB1" (0.000000)
330. feature "ZBED6CL" (0.000000)
331. feature "COBL" (0.000000)
332. feature "NKX6-1" (0.000000)
333. feature "BST2" (0.000000)
334. feature "TTC23L-AS1" (0.000000)
335. feature "SYNM" (0.000000)
336. feature "POU3F4" (0.000000)
337. feature "ZNF878" (0.000000)
338. feature "MYO3A" (0.000000)
339. feature "OSBP2" (0.000000)
340. feature "RTN4RL1" (0.000000)
341. feature "ADGRF1" (0.000000)
342. feature "IDUA" (0.000000)
343. feature "SYT3" (0.000000)
344. feature "CPNE5" (0.000000)
345. feature "C16orf86" (0.000000)
346. feature "RIPK3" (0.000000)
347. feature "ACADL" (0.000000)
348. feature "VIT" (0.000000)
349. feature "ABCA6" (0.000000)
350. feature "KIAA1549" (0.000000)
351. feature "TGFBR2" (0.000000)
352. feature "CPQ" (0.000000)
353. feature "GLDC" (0.000000)
354. feature "HECW2" (0.000000)
355. feature "HAP1" (0.000000)
356. feature "NR4A1" (0.000000)
357. feature "FAT4" (0.000000)
358. feature "RP1L1" (0.000000)
359. feature "FN1" (0.000000)
360. feature "F2RL3" (0.000000)
361. feature "GABRD" (0.000000)
362. feature "SCIN" (0.000000)
363. feature "AFM" (0.000000)
364. feature "ANLN" (0.000000)
365. feature "NOTCH3" (0.000000)
366. feature "CKAP2" (0.000000)
367. feature "PPM1M" (0.000000)
368. feature "CENPA" (0.000000)
369. feature "LYSMD1" (0.000000)
370. feature "ADD2" (0.000000)
371. feature "NTNG1" (0.000000)
372. feature "FEZF1" (0.000000)
373. feature "ZNF737" (0.000000)
374. feature "GHDC" (0.000000)
375. feature "BMP4" (0.000000)
376. feature "ZNF563" (0.000000)
377. feature "RACGAP1" (0.000000)
378. feature "CALML5" (0.000000)
379. feature "DNAJC22" (0.000000)
380. feature "LINC00628" (0.000000)
381. feature "LINC01843" (0.000000)
382. feature "SLC2A10" (0.000000)
383. feature "AGTR1" (0.000000)
384. feature "C7" (0.000000)
385. feature "RAET1L" (0.000000)
386. feature "ZNF22-AS1" (0.000000)
387. feature "PPM1J" (0.000000)
388. feature "FAM25A" (0.000000)
389. feature "SYTL2" (0.000000)
390. feature "ONECUT2" (0.000000)
391. feature "LRRK2" (0.000000)
392. feature "GPER1" (0.000000)
393. feature "KALRN" (0.000000)
394. feature "ARHGAP24" (0.000000)
395. feature "MUC7" (0.000000)
396. feature "RASSF10" (0.000000)
397. feature "FAM149A" (0.000000)
398. feature "RAB15" (0.000000)
399. feature "DLK2" (0.000000)
400. feature "LUM" (0.000000)
401. feature "CPED1" (0.000000)
402. feature "ZNF75D" (0.000000)
403. feature "PLA2G4A" (0.000000)
404. feature "ZNF185" (0.000000)
405. feature "PI3" (0.000000)
406. feature "RASL11B" (0.000000)
407. feature "CYP2R1" (0.000000)
408. feature "FRAT1" (0.000000)
409. feature "ASPM" (0.000000)
410. feature "GFPT1" (0.000000)
411. feature "STEAP4" (0.000000)
412. feature "FAM218A" (0.000000)
413. feature "PPP1R3C" (0.000000)
414. feature "STX11" (0.000000)
415. feature "LGALS1" (0.000000)
416. feature "IMPDH1P3" (0.000000)
417. feature "CD40" (0.000000)
418. feature "HLA-DQB1" (0.000000)
419. feature "B4GALNT3" (0.000000)
420. feature "SEC16B" (0.000000)
421. feature "CNRIP1" (0.000000)
422. feature "MPL" (0.000000)
423. feature "OLFM1" (0.000000)
424. feature "RGL1" (0.000000)
425. feature "ADRA2C" (0.000000)
426. feature "LINC02709" (0.000000)
427. feature "NWD1" (0.000000)
428. feature "P4HTM" (0.000000)
429. feature "LINC01819" (0.000000)
430. feature "SLC1A4" (0.000000)
431. feature "XK" (0.000000)
432. feature "LINC00184" (0.000000)
433. feature "FNDC5" (0.000000)
434. feature "RNF152" (0.000000)
435. feature "HEG1" (0.000000)
436. feature "RHOV" (0.000000)
437. feature "SIDT2" (0.000000)
438. feature "NOXA1" (0.000000)
439. feature "PCDHB6" (0.000000)
440. feature "USP27X" (0.000000)
441. feature "KIF19" (0.000000)
442. feature "EFCAB6-DT" (0.000000)
443. feature "OVGP1" (0.000000)
444. feature "SLC27A1" (0.000000)
445. feature "IL17RD" (0.000000)
446. feature "BMP6" (0.000000)
447. feature "ANXA9" (0.000000)
448. feature "P2RY6" (0.000000)
449. feature "LURAP1" (0.000000)
450. feature "KCNN3" (0.000000)
451. feature "SH3RF3" (0.000000)
452. feature "GLYCTK" (0.000000)
453. feature "LINC02036" (0.000000)
454. feature "NTF3" (0.000000)
455. feature "CNR1" (0.000000)
456. feature "TCEA3" (0.000000)
457. feature "KIF14" (0.000000)
458. feature "SPON2" (0.000000)
459. feature "FER1L5" (0.000000)
460. feature "HSPA8" (0.000000)
461. feature "CATSPER3" (0.000000)
462. feature "IL2RG" (0.000000)
463. feature "TNIP3" (0.000000)
464. feature "LINC02701" (0.000000)
465. feature "ACTG1" (0.000000)
466. feature "ZNF775" (0.000000)
467. feature "SLC6A17" (0.000000)
468. feature "OR51B8P" (0.000000)
469. feature "RPSAP41" (0.000000)
470. feature "NCR3LG1" (0.000000)
471. feature "ALOX5AP" (0.000000)
472. feature "ELOVL2" (0.000000)
473. feature "KRCC1" (0.000000)
474. feature "GCNA" (0.000000)
475. feature "TUBB4A" (0.000000)
476. feature "AKAP3" (0.000000)
477. feature "OSBPL6" (0.000000)
478. feature "C10orf143" (0.000000)
479. feature "ZNF805" (0.000000)
480. feature "FBF1" (0.000000)
481. feature "ZNF169" (0.000000)
482. feature "JUNB" (0.000000)
483. feature "CMPK2" (0.000000)
484. feature "AXIN2" (0.000000)
485. feature "FGF12" (0.000000)
486. feature "GRIK2" (0.000000)
487. feature "MYC" (0.000000)
488. feature "ZNF665" (0.000000)
489. feature "MYH3" (0.000000)
490. feature "GPR4" (0.000000)
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2976. feature "TNFRSF10D" (0.000000)
2977. feature "VIPR1-AS1" (0.000000)
2978. feature "PATL2" (0.000000)
2979. feature "LIMS2" (0.000000)
2980. feature "LINC01203" (0.000000)
2981. feature "KCNK2" (0.000000)
2982. feature "RAMP2" (0.000000)
2983. feature "OTULIN-DT" (0.000000)
2984. feature "DCLK1" (0.000000)
2985. feature "NPTN-IT1" (0.000000)
2986. feature "C11orf87" (0.000000)
2987. feature "ZNF214" (0.000000)
2988. feature "TSSK4" (0.000000)
2989. feature "APCDD1L" (0.000000)
2990. feature "TLR4" (0.000000)
2991. feature "COL9A2" (0.000000)
2992. feature "WARS1" (0.000000)
2993. feature "FAM47E" (0.000000)
2994. feature "EFNB3" (0.000000)
2995. feature "HMCN1" (0.000000)
2996. feature "FTLP12" (0.000000)
2997. feature "NINJ2" (0.000000)
2998. feature "E2F1" (0.000000)
2999. feature "MYH9" (0.000000)
3000. feature "SEC14L1P1" (0.000000)

¶

4.2.3) XGBoost

Since also experimenting with other hyperparameters (that are not show in the code before to maintain an acceptable running time) have not lead us to performance improving, we decide to experiment with the XGboost classifier.

XGBoost is an ensemble learning method similar to the random forest but it is specifically based on gradient boosting. It builds an ensemble of weak learners (usually and also in our case decision trees) sequentially, with each new learner focused on correcting the errors of the previous ones.

XGBoost often achieves even higher prediction accuracy compared to Random Forest, especially for structured/tabular data. It can effectively capture complex relationships between features and the target variable through gradient boosting and optimization techniques.

This was confirmed from the fact that the Xgboost classifier got a 100% score on the test dataset.

Comparing the list of most important features, we can see some differences in the first 20 places that have surely made this estimator perform better then the one before.

In [ ]:
#build the XGBoost classifier for the SmartSeq HCC1806 dataset
from xgboost import XGBClassifier

# Define the XGBoost classifier
xgb = XGBClassifier(random_state=42)

# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [10, 20, 50], 'max_depth': [10, 20, 30]}
grid_search = GridSearchCV(xgb, param_grid, cv=5)

# Define the leave one out cross validation
kfold = KFold(n_splits=5, shuffle=True, random_state=42)

# Perform the nested cross validation
scores = cross_val_score(grid_search, X_train_df_SS_HCC1806, y_train_df_SS_HCC1806, cv=kfold)

# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))

# Perform GridSearchCV on the train dataset to get the best parameters and fit the model
grid_search.fit(X_train_df_SS_HCC1806, y_train_df_SS_HCC1806)
print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
print("Best model: ", grid_search.best_estimator_)

#i want to test the model on the test dataset
print("Test accuracy: ", grid_search.best_estimator_.score(X_test_df_SS_HCC1806, y_test_df_SS_HCC1806))

# Print the feature importances
importances = grid_search.best_estimator_.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_df_SS_HCC1806.shape[1]):
    print("%d. feature %s (%f)" % (f + 1, X_train_df_SS_HCC1806.columns[indices[f]], importances[indices[f]]))

best_xgb_SS_HCC1806 = grid_search.best_estimator_

Mean accuracy of nested cross validation:  0.960923076923077
Best hyperparameters:  {'max_depth': 10, 'n_estimators': 10}
Best accuracy:  0.9378461538461538
Best model:  XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=10, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=10, n_jobs=None,
              num_parallel_tree=None, random_state=42, ...)
Test accuracy:  1.0
Feature ranking:
1. feature "NDRG1" (0.427974)
2. feature "BNIP3" (0.196460)
3. feature "PGK1" (0.130549)
4. feature "EGLN3" (0.054734)
5. feature "BHLHE40" (0.045408)
6. feature "FUT11" (0.026541)
7. feature "NEDD9" (0.024163)
8. feature "UBE2C" (0.022874)
9. feature "CA9" (0.020198)
10. feature "ENO2" (0.010031)
11. feature "PRDM1" (0.009949)
12. feature "SERPINE1" (0.007386)
13. feature "MIF-AS1" (0.006251)
14. feature "KDM5B" (0.005265)
15. feature "PLIN2" (0.003634)
16. feature "G0S2" (0.003282)
17. feature "ARL6IP1" (0.003138)
18. feature "CCNB1" (0.001757)
19. feature "S100A2" (0.000332)
20. feature "HTRA1" (0.000073)
21. feature "RAET1L" (0.000000)
22. feature "C7" (0.000000)
23. feature "AGTR1" (0.000000)
24. feature "KLF6" (0.000000)
25. feature "RP1L1" (0.000000)
26. feature "PPM1J" (0.000000)
27. feature "FAT4" (0.000000)
28. feature "GLUL" (0.000000)
29. feature "FAM25A" (0.000000)
30. feature "ZNF737" (0.000000)
31. feature "FEZF1" (0.000000)
32. feature "NTNG1" (0.000000)
33. feature "ADD2" (0.000000)
34. feature "HECW2" (0.000000)
35. feature "HAP1" (0.000000)
36. feature "NR4A1" (0.000000)
37. feature "GLDC" (0.000000)
38. feature "ODAD2" (0.000000)
39. feature "SLC2A10" (0.000000)
40. feature "LINC01843" (0.000000)
41. feature "ADCY6" (0.000000)
42. feature "LRRC2" (0.000000)
43. feature "SNCG" (0.000000)
44. feature "MMP13" (0.000000)
45. feature "ZNF311" (0.000000)
46. feature "PDE2A" (0.000000)
47. feature "STOX1" (0.000000)
48. feature "LINC00235" (0.000000)
49. feature "DGCR5" (0.000000)
50. feature "CPE" (0.000000)
51. feature "PLB1" (0.000000)
52. feature "ZBED6CL" (0.000000)
53. feature "TMEM187" (0.000000)
54. feature "TGFBR2" (0.000000)
55. feature "SCIN" (0.000000)
56. feature "BMP4" (0.000000)
57. feature "ZNF563" (0.000000)
58. feature "RACGAP1" (0.000000)
59. feature "ENPP5" (0.000000)
60. feature "GABRD" (0.000000)
61. feature "LINC00628" (0.000000)
62. feature "DNAJC22" (0.000000)
63. feature "MUC1" (0.000000)
64. feature "ANLN" (0.000000)
65. feature "IQGAP2" (0.000000)
66. feature "EPHA10" (0.000000)
67. feature "VAMP1" (0.000000)
68. feature "TUBA8" (0.000000)
69. feature "MSMO1" (0.000000)
70. feature "PLA2G4D" (0.000000)
71. feature "GLRA3" (0.000000)
72. feature "SOWAHA" (0.000000)
73. feature "PADI1" (0.000000)
74. feature "STAT4" (0.000000)
75. feature "CYP26C1" (0.000000)
76. feature "CMYA5" (0.000000)
77. feature "ANO9" (0.000000)
78. feature "UNC13B" (0.000000)
79. feature "RASL10A" (0.000000)
80. feature "KCNIP3" (0.000000)
81. feature "CXCL2" (0.000000)
82. feature "DNAJB5" (0.000000)
83. feature "ARRDC4" (0.000000)
84. feature "CDC45" (0.000000)
85. feature "CAND2" (0.000000)
86. feature "PELI2" (0.000000)
87. feature "NOTCH3" (0.000000)
88. feature "IFIT1" (0.000000)
89. feature "CKAP2" (0.000000)
90. feature "PPM1M" (0.000000)
91. feature "CENPA" (0.000000)
92. feature "CPQ" (0.000000)
93. feature "TFRC" (0.000000)
94. feature "ZNF425" (0.000000)
95. feature "MVP" (0.000000)
96. feature "PACSIN1" (0.000000)
97. feature "AURKC" (0.000000)
98. feature "EDIL3" (0.000000)
99. feature "SIGLEC15" (0.000000)
100. feature "LINC00161" (0.000000)
101. feature "ABAT" (0.000000)
102. feature "MC1R" (0.000000)
103. feature "ERICH5" (0.000000)
104. feature "SRXN1" (0.000000)
105. feature "ZNF853" (0.000000)
106. feature "ERO1A" (0.000000)
107. feature "NPR3" (0.000000)
108. feature "IGFBP1" (0.000000)
109. feature "AQP3" (0.000000)
110. feature "SLC30A3" (0.000000)
111. feature "EXOC3L4" (0.000000)
112. feature "MME" (0.000000)
113. feature "NACAD" (0.000000)
114. feature "MMP2" (0.000000)
115. feature "TMCC2" (0.000000)
116. feature "AATBC" (0.000000)
117. feature "DISC1" (0.000000)
118. feature "CALR3" (0.000000)
119. feature "CTH" (0.000000)
120. feature "LINC02154" (0.000000)
121. feature "CLCA4" (0.000000)
122. feature "SMURF2" (0.000000)
123. feature "PIM1" (0.000000)
124. feature "GSC" (0.000000)
125. feature "ENTPD3" (0.000000)
126. feature "DSC2" (0.000000)
127. feature "DOK3" (0.000000)
128. feature "TRIM34" (0.000000)
129. feature "LIFR-AS1" (0.000000)
130. feature "SSTR2" (0.000000)
131. feature "NAT8L" (0.000000)
132. feature "FAM27E5" (0.000000)
133. feature "IGSF9B" (0.000000)
134. feature "COBL" (0.000000)
135. feature "DDO" (0.000000)
136. feature "PLAAT2" (0.000000)
137. feature "KRT18P63" (0.000000)
138. feature "LINC00865" (0.000000)
139. feature "ADRB2" (0.000000)
140. feature "MTNR1A" (0.000000)
141. feature "PSAP" (0.000000)
142. feature "FGF1" (0.000000)
143. feature "AK5" (0.000000)
144. feature "RNF180" (0.000000)
145. feature "FBN1" (0.000000)
146. feature "SNAI3" (0.000000)
147. feature "ZNF319" (0.000000)
148. feature "EIF5" (0.000000)
149. feature "UHRF1" (0.000000)
150. feature "WDR81" (0.000000)
151. feature "ITGA6" (0.000000)
152. feature "GBP6" (0.000000)
153. feature "FYN" (0.000000)
154. feature "SLC19A3" (0.000000)
155. feature "DMBX1" (0.000000)
156. feature "AOAH" (0.000000)
157. feature "DSCAM" (0.000000)
158. feature "OTUB2" (0.000000)
159. feature "SYT3" (0.000000)
160. feature "CEP83-DT" (0.000000)
161. feature "BEST3" (0.000000)
162. feature "ZNF610" (0.000000)
163. feature "ZNF415" (0.000000)
164. feature "GBP2" (0.000000)
165. feature "NPNT" (0.000000)
166. feature "POU3F4" (0.000000)
167. feature "OSBP2" (0.000000)
168. feature "PKP1" (0.000000)
169. feature "CPNE5" (0.000000)
170. feature "CASC19" (0.000000)
171. feature "C16orf86" (0.000000)
172. feature "RIPK3" (0.000000)
173. feature "ACADL" (0.000000)
174. feature "VIT" (0.000000)
175. feature "THSD7A" (0.000000)
176. feature "ABCA6" (0.000000)
177. feature "TTC23L-AS1" (0.000000)
178. feature "BST2" (0.000000)
179. feature "NKX6-1" (0.000000)
180. feature "LHX2" (0.000000)
181. feature "THSD7B" (0.000000)
182. feature "VSIG1" (0.000000)
183. feature "RGS16" (0.000000)
184. feature "PRDM9" (0.000000)
185. feature "GADD45A" (0.000000)
186. feature "NAALAD2" (0.000000)
187. feature "SYT16" (0.000000)
188. feature "FAM111B" (0.000000)
189. feature "DCXR-DT" (0.000000)
190. feature "TRPC4" (0.000000)
191. feature "ZFP41" (0.000000)
192. feature "SERPINB11" (0.000000)
193. feature "WNT4" (0.000000)
194. feature "ZNF221" (0.000000)
195. feature "GEM" (0.000000)
196. feature "IL6R-AS1" (0.000000)
197. feature "CCN3" (0.000000)
198. feature "MIXL1" (0.000000)
199. feature "CACFD1" (0.000000)
200. feature "TEP1" (0.000000)
201. feature "TTC28" (0.000000)
202. feature "SEMA4B" (0.000000)
203. feature "MAG" (0.000000)
204. feature "SMIM5" (0.000000)
205. feature "ZBTB26" (0.000000)
206. feature "ZNF182" (0.000000)
207. feature "NTF3" (0.000000)
208. feature "IL2RG" (0.000000)
209. feature "LINC02701" (0.000000)
210. feature "ACTG1" (0.000000)
211. feature "ZNF775" (0.000000)
212. feature "SLC6A17" (0.000000)
213. feature "RPSAP41" (0.000000)
214. feature "TNIP3" (0.000000)
215. feature "ZWINT" (0.000000)
216. feature "RNF152" (0.000000)
217. feature "LINC01819" (0.000000)
218. feature "HEG1" (0.000000)
219. feature "RHOV" (0.000000)
220. feature "SIDT2" (0.000000)
221. feature "NOXA1" (0.000000)
222. feature "PCDHB6" (0.000000)
223. feature "USP27X" (0.000000)
224. feature "EFCAB6-DT" (0.000000)
225. feature "OVGP1" (0.000000)
226. feature "SLC27A1" (0.000000)
227. feature "CATSPER3" (0.000000)
228. feature "PRSS23" (0.000000)
229. feature "SPON2" (0.000000)
230. feature "KIF14" (0.000000)
231. feature "STEAP4" (0.000000)
232. feature "GFPT1" (0.000000)
233. feature "ARHGAP24" (0.000000)
234. feature "MUC7" (0.000000)
235. feature "RASSF10" (0.000000)
236. feature "DLK2" (0.000000)
237. feature "LUM" (0.000000)
238. feature "ZNF75D" (0.000000)
239. feature "ZNF185" (0.000000)
240. feature "PI3" (0.000000)
241. feature "CYP2R1" (0.000000)
242. feature "FRAT1" (0.000000)
243. feature "ASPM" (0.000000)
244. feature "SLC1A4" (0.000000)
245. feature "GPER1" (0.000000)
246. feature "XK" (0.000000)
247. feature "FNDC5" (0.000000)
248. feature "CNR1" (0.000000)
249. feature "HBP1" (0.000000)
250. feature "PFKFB4" (0.000000)
251. feature "BMP6" (0.000000)
252. feature "ANXA9" (0.000000)
253. feature "CLIC3" (0.000000)
254. feature "CES4A" (0.000000)
255. feature "ACKR2" (0.000000)
256. feature "KCTD8" (0.000000)
257. feature "ANXA10" (0.000000)
258. feature "MYH14" (0.000000)
259. feature "CPLANE2" (0.000000)
260. feature "CD14" (0.000000)
261. feature "CLCN5" (0.000000)
262. feature "MALAT1" (0.000000)
263. feature "SPATA12" (0.000000)
264. feature "GGT1" (0.000000)
265. feature "IL1RL1" (0.000000)
266. feature "SDK1" (0.000000)
267. feature "SLCO4C1" (0.000000)
268. feature "ITGB2" (0.000000)
269. feature "LIPA" (0.000000)
270. feature "SP6" (0.000000)
271. feature "ALDH1A3" (0.000000)
272. feature "DGKQ" (0.000000)
273. feature "DMRTA1" (0.000000)
274. feature "CD86" (0.000000)
275. feature "P2RY6" (0.000000)
276. feature "DAPK2" (0.000000)
277. feature "LURAP1" (0.000000)
278. feature "KCNN3" (0.000000)
279. feature "ZSCAN20" (0.000000)
280. feature "TNFRSF1B" (0.000000)
281. feature "ABCA3" (0.000000)
282. feature "LINC00858" (0.000000)
283. feature "GAS7" (0.000000)
284. feature "BCL6" (0.000000)
285. feature "PRSS21" (0.000000)
286. feature "KRT18" (0.000000)
287. feature "IL12RB2" (0.000000)
288. feature "TAGLN" (0.000000)
289. feature "DDIT3" (0.000000)
290. feature "RAPGEFL1" (0.000000)
291. feature "TSPAN8" (0.000000)
292. feature "AZIN2" (0.000000)
293. feature "ASB2" (0.000000)
294. feature "SPOCK2" (0.000000)
295. feature "CLMN" (0.000000)
296. feature "FAM218A" (0.000000)
297. feature "P4HTM" (0.000000)
298. feature "CD99L2" (0.000000)
299. feature "FGF12" (0.000000)
300. feature "GREM2" (0.000000)
301. feature "TMEM74B" (0.000000)
302. feature "TUBB4A" (0.000000)
303. feature "C10orf143" (0.000000)
304. feature "ZNF805" (0.000000)
305. feature "ZNF169" (0.000000)
306. feature "JUNB" (0.000000)
307. feature "CMPK2" (0.000000)
308. feature "AXIN2" (0.000000)
309. feature "MYC" (0.000000)
310. feature "NWD1" (0.000000)
311. feature "ZNF665" (0.000000)
312. feature "MYH3" (0.000000)
313. feature "GLIPR2" (0.000000)
314. feature "CHN1" (0.000000)
315. feature "ZNF697" (0.000000)
316. feature "LINC02065" (0.000000)
317. feature "WNT10B" (0.000000)
318. feature "SHROOM4" (0.000000)
319. feature "N4BP2L2-IT2" (0.000000)
320. feature "CECR2" (0.000000)
321. feature "KDM8" (0.000000)
322. feature "HSPA1L" (0.000000)
323. feature "P2RX7" (0.000000)
324. feature "MAPK8IP2" (0.000000)
325. feature "SESN2" (0.000000)
326. feature "IL17RB" (0.000000)
327. feature "DGAT2" (0.000000)
328. feature "C10orf55" (0.000000)
329. feature "ZSWIM3" (0.000000)
330. feature "EPG5" (0.000000)
331. feature "ZFP36L1" (0.000000)
332. feature "SYTL2" (0.000000)
333. feature "F2RL3" (0.000000)
334. feature "LINC00184" (0.000000)
335. feature "LINC01134" (0.000000)
336. feature "CRIP2" (0.000000)
337. feature "GABRQ" (0.000000)
338. feature "STRA6" (0.000000)
339. feature "PTGES" (0.000000)
340. feature "S1PR3" (0.000000)
341. feature "IL1R1" (0.000000)
342. feature "H1-0" (0.000000)
343. feature "LRRIQ4" (0.000000)
344. feature "LOX" (0.000000)
345. feature "ANKRD31" (0.000000)
346. feature "HTR2C" (0.000000)
347. feature "ATF3" (0.000000)
348. feature "SLC9A4" (0.000000)
349. feature "GIMAP2" (0.000000)
350. feature "GCNA" (0.000000)
351. feature "KRCC1" (0.000000)
352. feature "ELOVL2" (0.000000)
353. feature "ALOX5AP" (0.000000)
354. feature "STX11" (0.000000)
355. feature "LGALS1" (0.000000)
356. feature "IMPDH1P3" (0.000000)
357. feature "CD40" (0.000000)
358. feature "HLA-DQB1" (0.000000)
359. feature "B4GALNT3" (0.000000)
360. feature "PPP1R3C" (0.000000)
361. feature "CNRIP1" (0.000000)
362. feature "OLFM1" (0.000000)
363. feature "RGL1" (0.000000)
364. feature "ADRA2C" (0.000000)
365. feature "LINC02709" (0.000000)
366. feature "KCNQ2" (0.000000)
367. feature "TNFSF14" (0.000000)
368. feature "CYS1" (0.000000)
369. feature "MYO7A" (0.000000)
370. feature "LINC02635" (0.000000)
371. feature "SALL4" (0.000000)
372. feature "ANKRD34A" (0.000000)
373. feature "C5" (0.000000)
374. feature "BDKRB2" (0.000000)
375. feature "CYP24A1" (0.000000)
376. feature "RHOBTB1" (0.000000)
377. feature "MMP16" (0.000000)
378. feature "ASF1B" (0.000000)
379. feature "DOCK8-AS1" (0.000000)
380. feature "ZNF225" (0.000000)
381. feature "SQSTM1" (0.000000)
382. feature "PPP1R15A" (0.000000)
383. feature "QRFPR" (0.000000)
384. feature "IL11RA" (0.000000)
385. feature "DDX60" (0.000000)
386. feature "GAREM1" (0.000000)
387. feature "SNAI1" (0.000000)
388. feature "SPOCK3" (0.000000)
389. feature "IL31RA" (0.000000)
390. feature "ILDR1" (0.000000)
391. feature "C1QTNF12" (0.000000)
392. feature "ODAD4" (0.000000)
393. feature "SYNM" (0.000000)
394. feature "LINC00898" (0.000000)
395. feature "KRT222" (0.000000)
396. feature "ACY3" (0.000000)
397. feature "CD59" (0.000000)
398. feature "APOL1" (0.000000)
399. feature "ATP6V1B1" (0.000000)
400. feature "SOX8" (0.000000)
401. feature "RND2" (0.000000)
402. feature "FNDC4" (0.000000)
403. feature "TNNC1" (0.000000)
404. feature "HSD11B1" (0.000000)
405. feature "ZCWPW1" (0.000000)
406. feature "CAMK2B" (0.000000)
407. feature "RINL" (0.000000)
408. feature "PRSS53" (0.000000)
409. feature "PINK1" (0.000000)
410. feature "BVES" (0.000000)
411. feature "ZNF767P" (0.000000)
412. feature "RPL10P2" (0.000000)
413. feature "ZNF878" (0.000000)
414. feature "MYO3A" (0.000000)
415. feature "RTN4RL1" (0.000000)
416. feature "ADGRF1" (0.000000)
417. feature "UGT2B7" (0.000000)
418. feature "LRRK2" (0.000000)
419. feature "KRT7" (0.000000)
420. feature "PPP4R4" (0.000000)
421. feature "EFEMP1" (0.000000)
422. feature "AGMO" (0.000000)
423. feature "TCHH" (0.000000)
424. feature "PPP1R36" (0.000000)
425. feature "THOC1-DT" (0.000000)
426. feature "GDPD3" (0.000000)
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2915. feature "FZD4-DT" (0.000000)
2916. feature "CNTN1" (0.000000)
2917. feature "CDKN2B-AS1" (0.000000)
2918. feature "IRAK2" (0.000000)
2919. feature "APLP1" (0.000000)
2920. feature "AKR7A3" (0.000000)
2921. feature "RAMP2" (0.000000)
2922. feature "EPCAM-DT" (0.000000)
2923. feature "TTK" (0.000000)
2924. feature "SH3BP5L" (0.000000)
2925. feature "DCBLD2" (0.000000)
2926. feature "LINC00243" (0.000000)
2927. feature "MAP4K1" (0.000000)
2928. feature "PALM2AKAP2" (0.000000)
2929. feature "GUCY1B1" (0.000000)
2930. feature "ZKSCAN3" (0.000000)
2931. feature "ZCCHC2" (0.000000)
2932. feature "QRSL1P3" (0.000000)
2933. feature "SETBP1-DT" (0.000000)
2934. feature "HSP90B1" (0.000000)
2935. feature "SEC61A1" (0.000000)
2936. feature "RPL7AP10" (0.000000)
2937. feature "GPR158" (0.000000)
2938. feature "KLRA1P" (0.000000)
2939. feature "ZNF711" (0.000000)
2940. feature "RIBC1" (0.000000)
2941. feature "ALOX15" (0.000000)
2942. feature "LINC00571" (0.000000)
2943. feature "ZNF398" (0.000000)
2944. feature "KIAA0040" (0.000000)
2945. feature "LINC00518" (0.000000)
2946. feature "TAS2R2P" (0.000000)
2947. feature "HPDL" (0.000000)
2948. feature "IMPDH1P10" (0.000000)
2949. feature "TCP10L" (0.000000)
2950. feature "PFKL" (0.000000)
2951. feature "LINC01833" (0.000000)
2952. feature "STK31" (0.000000)
2953. feature "BCAR3" (0.000000)
2954. feature "PAX7" (0.000000)
2955. feature "LINC01270" (0.000000)
2956. feature "CDCA3" (0.000000)
2957. feature "YPEL5" (0.000000)
2958. feature "RIMS3" (0.000000)
2959. feature "LINC01752" (0.000000)
2960. feature "SLAMF8" (0.000000)
2961. feature "TUBA1B" (0.000000)
2962. feature "PCED1B-AS1" (0.000000)
2963. feature "FABP3" (0.000000)
2964. feature "PIGZ" (0.000000)
2965. feature "BCRP9" (0.000000)
2966. feature "TDRKH-AS1" (0.000000)
2967. feature "ZNF473" (0.000000)
2968. feature "PPM1L" (0.000000)
2969. feature "TMEM249" (0.000000)
2970. feature "ADCY10" (0.000000)
2971. feature "LINC01956" (0.000000)
2972. feature "PLEKHA7" (0.000000)
2973. feature "CYYR1" (0.000000)
2974. feature "SH2D2A" (0.000000)
2975. feature "SLC1A3" (0.000000)
2976. feature "FBXO43" (0.000000)
2977. feature "ZC3H12A" (0.000000)
2978. feature "TACSTD2" (0.000000)
2979. feature "KLHL22" (0.000000)
2980. feature "HES1" (0.000000)
2981. feature "SLIT1" (0.000000)
2982. feature "CCK" (0.000000)
2983. feature "TP53I11" (0.000000)
2984. feature "HLA-DOB" (0.000000)
2985. feature "TRPV4" (0.000000)
2986. feature "CAMK1D" (0.000000)
2987. feature "CRABP2" (0.000000)
2988. feature "LYPD5" (0.000000)
2989. feature "ZNF517" (0.000000)
2990. feature "HLX" (0.000000)
2991. feature "SERPINF2" (0.000000)
2992. feature "FTCDNL1" (0.000000)
2993. feature "KRT80" (0.000000)
2994. feature "MSC" (0.000000)
2995. feature "LINC02085" (0.000000)
2996. feature "CCDC96" (0.000000)
2997. feature "RNASE7" (0.000000)
2998. feature "RHBG" (0.000000)
2999. feature "NQO1" (0.000000)
3000. feature "DDIT4" (0.000000)

¶

4.2.4) KNN

In [ ]:
best_knn_SS_HCC1806=train_and_evaluate_knn(X_train_SS_HCC, X_test_SS_HCC, y_train_SS_HCC, y_test_SS_HCC)

Best hyperparameters:  {'n_neighbors': 3}
Accuracy: 0.7837837837837838
F1: 0.7894736842105263
Classification Report:
              precision    recall  f1-score   support

         0.0       0.64      1.00      0.78        14
         1.0       1.00      0.65      0.79        23

    accuracy                           0.78        37
   macro avg       0.82      0.83      0.78        37
weighted avg       0.86      0.78      0.79        37

¶

4.2.5) Logistic Regression

In [ ]:
best_logreg_SS_HCC1806=train_and_evaluate_logreg(X_train_SS_HCC, X_test_SS_HCC, y_train_SS_HCC, y_test_SS_HCC)

Best hyperparameters:  {'C': 0.002, 'penalty': 'l2'}
Accuracy: 1.0
F1: 1.0
Classification Report:
              precision    recall  f1-score   support

         0.0       1.00      1.00      1.00        14
         1.0       1.00      1.00      1.00        23

    accuracy                           1.00        37
   macro avg       1.00      1.00      1.00        37
weighted avg       1.00      1.00      1.00        37
In [ ]:
# try pca method to reduce number of features and use log log regression
In [ ]:
X_train_pca_SS_HCC, X_test_pca_SS_HCC, y_train_SS_HCC, y_test_SS_HCC,scaler_SS_HCC,pca_SS_HCC = prepare_data_pca(df_trans_SS_HCC)
In [ ]:
X_train_pca_SS_HCC.shape
Out[ ]:
(145, 89)
In [ ]:
model_logreg_hcc_SMART_ver2_SS_HCC=train_and_evaluate_logreg(X_train_pca_SS_HCC, X_test_pca_SS_HCC, y_train_SS_HCC, y_test_SS_HCC)

Best hyperparameters:  {'C': 0.002, 'penalty': 'l2'}
Accuracy: 1.0
F1: 1.0
Classification Report:
              precision    recall  f1-score   support

         0.0       1.00      1.00      1.00        14
         1.0       1.00      1.00      1.00        23

    accuracy                           1.00        37
   macro avg       1.00      1.00      1.00        37
weighted avg       1.00      1.00      1.00        37

¶

4.2.6) CatBoost

In [ ]:
best_catboost_SS_HCC1806=train_and_evaluate_catboost(X_train_SS_HCC, X_test_SS_HCC, y_train_SS_HCC, y_test_SS_HCC)

Best hyperparameters:  {'logging_level': 'Silent'}
Accuracy: 1.0
F1: 1.0
Classification Report:
              precision    recall  f1-score   support

         0.0       1.00      1.00      1.00        14
         1.0       1.00      1.00      1.00        23

    accuracy                           1.00        37
   macro avg       1.00      1.00      1.00        37
weighted avg       1.00      1.00      1.00        37
In [ ]:
new_columns_names_SS_HCC = []
for i in range(1,3001):
    name_new_SS_HCC = f'f{i}'
    new_columns_names_SS_HCC.append(name_new_SS_HCC)
X_test_pd_SS_HCC=pd.DataFrame(X_test_SS_HCC, columns=new_columns_names_SS_HCC)
X_train_pd_SS_HCC=pd.DataFrame(X_train_SS_HCC, columns=new_columns_names_SS_HCC)
In [ ]:
model_catboost_hcc_SMART_ver2_SS_HCC,cols_cut_SS_HCC=train_and_evaluate_catboost2(X_train_pd_SS_HCC, X_test_pd_SS_HCC, y_train_SS_HCC, y_test_SS_HCC)

1174
Best hyperparameters:  {'l2_leaf_reg': 3, 'logging_level': 'Silent', 'n_estimators': 500}
Accuracy: 1.0
F1: 1.0
Classification Report:
              precision    recall  f1-score   support

         0.0       1.00      1.00      1.00        14
         1.0       1.00      1.00      1.00        23

    accuracy                           1.00        37
   macro avg       1.00      1.00      1.00        37
weighted avg       1.00      1.00      1.00        37

¶

4.3) PREDICTORS DROPSEQ MCF7

We apply the same steps of the dataset before (see 4.1) to the DropSeq MCF7 dataset

In [ ]:
#load the DropSeq MCF7 dataset
df_DS_MCF7_norm_filt = pd.read_csv("DropSeq\MCF7_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ",engine='python',index_col=0)

df_DS_MCF7_norm_filtT = df_DS_MCF7_norm_filt.T

# Add the target column
df_DS_MCF7_norm_filtT['target'] = np.where(df_DS_MCF7_norm_filtT.index.str.contains('Hypo'), 1, 0)

#i want to split the data into training and testing data, with a balance of 70% training and 30% testing

X_df_DS_MCF7 = df_DS_MCF7_norm_filtT.drop('target', axis=1)
y_df_DS_MCF7 = df_DS_MCF7_norm_filtT['target']

X_train_df_DS_MCF7, X_test_df_DS_MCF7, y_train_df_DS_MCF7, y_test_df_DS_MCF7 = train_test_split(X_df_DS_MCF7, y_df_DS_MCF7, test_size=0.2, random_state=42)
In [ ]:
df_basic_DS_MCF=df_DS_MCF7_norm_filt
df_trans_DS_MCF=df_basic_DS_MCF.T
df_trans_DS_MCF['TARGET'] = df_trans_DS_MCF.columns.to_series().apply(lambda x: 1 if 'Hypo' in x else 0)
df_trans_DS_MCF.loc[df_trans_DS_MCF.index.str.contains('Norm'), 'TARGET'] = 0
df_trans_DS_MCF.loc[df_trans_DS_MCF.index.str.contains('Hypo'), 'TARGET'] = 1
In [ ]:
X_train_DS_MCF, X_test_DS_MCF, y_train_DS_MCF, y_test_DS_MCF,scaler_DS_MCF = prepare_data(df_trans_DS_MCF)

¶

4.3.1) SVM

We used same approach used for SmartSeq MCF7, see 4.1.1.

In [ ]:
# build SVM on DropSeq MCF7

# Define the SVM classifier
svm = SVC(random_state=42)

# Define the parameter grid for GridSearchCV
param_grid = {'kernel': ['linear', 'rbf']}
grid_search_svm = GridSearchCV(svm, param_grid, cv=2)

# Define the leave one out cross validation
#loo = LeaveOneOut()

# Perform the n
# ested cross validation
scores_svm = cross_val_score(grid_search_svm, X_train_df_DS_MCF7, y_train_df_DS_MCF7, cv=2)

# Print the mean accuracy of the nested cross validation
print("Mean accuracy of 2 fold cross validation: ", np.mean(scores_svm))

Mean accuracy of 2 fold cross validation:  0.9778034682080925
In [ ]:
# Perform GridSearchCV on the train dataset to get the best parameters and fit the model
grid_search_svm.fit(X_train_df_DS_MCF7, y_train_df_DS_MCF7)
print("Best kernel: ", grid_search_svm.best_params_)
print("Best accuracy: ", grid_search_svm.best_score_)
print("Best model: ", grid_search_svm.best_estimator_)
best_svm_DS_MCF7 = grid_search_svm.best_estimator_

# Test the model on the test dataset
print("Test accuracy: ", best_svm_DS_MCF7.score(X_test_df_DS_MCF7, y_test_df_DS_MCF7))

Best kernel:  {'kernel': 'rbf'}
Best accuracy:  0.9778034682080925
Best model:  SVC(random_state=42)
Test accuracy:  0.9798890429958391

¶

4.3.2) Random Forest

Here we build the random forest classifier for the DropSeq MCF7 dataset.

We followed also here the same steps as before but with a different approach in the cross validation.

Since the dataset is very big we decided to substitute the LeaveOneOut validation with a normal k-fold validation.

Another slight difference can be seen in the number of parameters, where the search was done for 50, 60, 100 estimators.

Also the max depth was sligtly augmented in the search to 30, 50, 60.

We point out also the fact that augmenting the parameters and the max depth didn't lead our model to overfit as the mean accuracy in the cross validation is high.

The overall performance of the model in the test set is 96,50%.

Something interesting can be seen in the feature ranking list, we noticed that the gene MT_RNR2 was the second most important, and this confiermed us that the intuition we had to look at the genes that seemed to have the highest difference in mean between the two samples was indeed a good way to explore the data.

In [ ]:
#i build the random forest classifier for the DropSeq MCF7 dataset

# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [50, 60, 100], 'max_depth': [30, 50, 60]}
grid_search = GridSearchCV(rf, param_grid, cv=5)

outer_cv = KFold(n_splits=5, shuffle=True, random_state=42)

# Perform the cross validation
scores = cross_val_score(grid_search, X_train_df_DS_MCF7, y_train_df_DS_MCF7, cv=outer_cv)

# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))    

# Perform GridSearchCV on the entire dataset to get the best parameters and fit the model
grid_search.fit(X_train_df_DS_MCF7, y_train_df_DS_MCF7)
print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
print("Best model: ", grid_search.best_estimator_)

#i want to test the model on the test dataset
print("Test accuracy: ", grid_search.best_estimator_.score(X_test_df_DS_MCF7, y_test_df_DS_MCF7))

# Print the feature importances
importances = grid_search.best_estimator_.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_df_DS_MCF7.shape[1]):
    print("%d. feature %s (%f)" % (f + 1, X_train_df_DS_MCF7.columns[indices[f]], importances[indices[f]]))

best_rf_DS_HCC1806 = grid_search.best_estimator_

Mean accuracy of nested cross validation:  0.9651871815047226
Best hyperparameters:  {'max_depth': 50, 'n_estimators': 100}
Best accuracy:  0.9656491183051349
Best model:  RandomForestClassifier(max_depth=50, random_state=42)
Test accuracy:  0.9650123304562269
Feature ranking:
1. feature "TFF1" (0.038533)
2. feature "MT-RNR2" (0.033664)
3. feature "KRT19" (0.030886)
4. feature "PGK1" (0.027427)
5. feature "GAPDH" (0.024421)
6. feature "TMSB10" (0.023137)
7. feature "RPL13" (0.021265)
8. feature "TPI1" (0.018440)
9. feature "RPS14" (0.018437)
10. feature "RPS19" (0.018002)
11. feature "MT-RNR1" (0.017737)
12. feature "LGALS1" (0.016023)
13. feature "RPL15" (0.015649)
14. feature "ENO1" (0.012904)
15. feature "PKM" (0.012042)
16. feature "RPL35" (0.011413)
17. feature "RPL12" (0.011380)
18. feature "RPL34" (0.011354)
19. feature "RPL36" (0.009996)
20. feature "MALAT1" (0.009275)
21. feature "TMEM258" (0.009189)
22. feature "RPS15A" (0.009098)
23. feature "RPL30" (0.009027)
24. feature "TFF3" (0.008791)
25. feature "RPLP1" (0.008760)
26. feature "HES1" (0.008350)
27. feature "MT-ND4" (0.008284)
28. feature "RPL39" (0.008220)
29. feature "RPS5" (0.008115)
30. feature "BCYRN1" (0.008098)
31. feature "ROMO1" (0.007435)
32. feature "RPL35A" (0.006409)
33. feature "RPLP2" (0.006336)
34. feature "H4C3" (0.006299)
35. feature "S100A10" (0.006285)
36. feature "RPS15" (0.006232)
37. feature "SNRPD2" (0.006232)
38. feature "SERF2" (0.006149)
39. feature "RPL23" (0.006011)
40. feature "DSP" (0.005712)
41. feature "HSPB1" (0.005571)
42. feature "RPS16" (0.005532)
43. feature "RPS12" (0.005227)
44. feature "TOB1" (0.004840)
45. feature "S100A11" (0.004817)
46. feature "KRT18" (0.004796)
47. feature "RPS27" (0.004792)
48. feature "ELOB" (0.004686)
49. feature "COX7A2" (0.004679)
50. feature "KRT8" (0.004648)
51. feature "SET" (0.004566)
52. feature "FTL" (0.004459)
53. feature "UBA52" (0.004420)
54. feature "ACTB" (0.004247)
55. feature "UQCRQ" (0.004241)
56. feature "MT-ND3" (0.004118)
57. feature "COX7C" (0.004041)
58. feature "ATP5MK" (0.003989)
59. feature "MT-CYB" (0.003911)
60. feature "MT-CO2" (0.003723)
61. feature "MT-CO3" (0.003549)
62. feature "RPL37A" (0.003418)
63. feature "NDUFB2" (0.003290)
64. feature "TMEM64" (0.003227)
65. feature "SOX4" (0.002900)
66. feature "MT-ATP6" (0.002865)
67. feature "DSCAM-AS1" (0.002821)
68. feature "FGF23" (0.002609)
69. feature "ATP5ME" (0.002569)
70. feature "RPL37" (0.002447)
71. feature "RPS28" (0.002436)
72. feature "PARD6B" (0.002406)
73. feature "MT2A" (0.002355)
74. feature "TRIM37" (0.002043)
75. feature "BCAS3" (0.001888)
76. feature "MT-ND4L" (0.001868)
77. feature "RPL11" (0.001852)
78. feature "RPL29" (0.001851)
79. feature "PKIB" (0.001829)
80. feature "PRRC2C" (0.001771)
81. feature "MT-ND5" (0.001688)
82. feature "TPD52L1" (0.001650)
83. feature "NEDD8" (0.001644)
84. feature "C4orf48" (0.001631)
85. feature "NCL" (0.001606)
86. feature "H2AC12" (0.001577)
87. feature "PFDN4" (0.001566)
88. feature "RPL28" (0.001526)
89. feature "CSDE1" (0.001513)
90. feature "H2AC20" (0.001491)
91. feature "EIF4G2" (0.001475)
92. feature "HSPD1" (0.001469)
93. feature "TPM4" (0.001435)
94. feature "GOLGA4" (0.001434)
95. feature "DNAJA1" (0.001362)
96. feature "NEAT1" (0.001342)
97. feature "IGFBP5" (0.001327)
98. feature "ANP32B" (0.001318)
99. feature "H1-4" (0.001279)
100. feature "NCOA3" (0.001248)
101. feature "ATP1A1" (0.001226)
102. feature "YWHAZ" (0.001219)
103. feature "YWHAB" (0.001216)
104. feature "HDGF" (0.001206)
105. feature "GPM6A" (0.001179)
106. feature "MT-ND2" (0.001158)
107. feature "MT-CO1" (0.001157)
108. feature "CAPZA1" (0.001155)
109. feature "VMP1" (0.001140)
110. feature "CALM2" (0.001128)
111. feature "PABPC1" (0.001122)
112. feature "TPM3" (0.001113)
113. feature "CKS2" (0.001111)
114. feature "PSMA7" (0.001101)
115. feature "HNRNPU" (0.001080)
116. feature "CBX3" (0.001075)
117. feature "RPS6KB1" (0.001059)
118. feature "HNRNPA2B1" (0.001057)
119. feature "H1-2" (0.001026)
120. feature "H1-5" (0.001009)
121. feature "RAD23B" (0.001005)
122. feature "MT-ND6" (0.000966)
123. feature "PTP4A2" (0.000947)
124. feature "DEK" (0.000938)
125. feature "BPTF" (0.000936)
126. feature "AREG" (0.000916)
127. feature "GATA3" (0.000907)
128. feature "PAK2" (0.000906)
129. feature "HNRNPAB" (0.000906)
130. feature "LARP4B" (0.000897)
131. feature "HSPH1" (0.000892)
132. feature "PDAP1" (0.000884)
133. feature "H1-3" (0.000874)
134. feature "MKI67" (0.000871)
135. feature "EIF3A" (0.000854)
136. feature "RHOBTB3" (0.000848)
137. feature "H2AC11" (0.000846)
138. feature "EIF5B" (0.000810)
139. feature "PPM1G" (0.000807)
140. feature "ARF3" (0.000802)
141. feature "ARPP19" (0.000798)
142. feature "STRBP" (0.000788)
143. feature "SLC9A3R1" (0.000769)
144. feature "PTPN11" (0.000767)
145. feature "ATAD2" (0.000750)
146. feature "NSD1" (0.000745)
147. feature "C7orf50" (0.000737)
148. feature "POLR2I" (0.000736)
149. feature "METAP2" (0.000720)
150. feature "RPS27L" (0.000717)
151. feature "NBN" (0.000711)
152. feature "PAPOLA" (0.000703)
153. feature "COPS9" (0.000700)
154. feature "H19" (0.000698)
155. feature "BAZ1B" (0.000692)
156. feature "HIF1A" (0.000692)
157. feature "CENPF" (0.000688)
158. feature "TOP2A" (0.000680)
159. feature "SMC4" (0.000669)
160. feature "XBP1" (0.000666)
161. feature "GNL3L" (0.000663)
162. feature "SLC25A24" (0.000662)
163. feature "LXN" (0.000658)
164. feature "MT-ND1" (0.000651)
165. feature "SPATS2L" (0.000648)
166. feature "POLR2J" (0.000641)
167. feature "AP2S1" (0.000637)
168. feature "GSPT1" (0.000630)
169. feature "MAN1A2" (0.000630)
170. feature "S100P" (0.000626)
171. feature "EIF3J" (0.000601)
172. feature "RBM25" (0.000592)
173. feature "SVIP" (0.000592)
174. feature "TPM1" (0.000590)
175. feature "C9orf78" (0.000588)
176. feature "TIMM8B" (0.000586)
177. feature "CDV3" (0.000577)
178. feature "NDUFS6" (0.000574)
179. feature "NCALD" (0.000574)
180. feature "H1-0" (0.000573)
181. feature "HMGB2" (0.000569)
182. feature "PRRG3" (0.000560)
183. feature "ZNF217" (0.000557)
184. feature "CAST" (0.000557)
185. feature "WDR43" (0.000553)
186. feature "HNRNPD" (0.000549)
187. feature "ANKRD11" (0.000543)
188. feature "AURKA" (0.000534)
189. feature "ZNF121" (0.000525)
190. feature "ZC3H15" (0.000523)
191. feature "BASP1" (0.000523)
192. feature "GTF3A" (0.000514)
193. feature "RAD21" (0.000513)
194. feature "TBCA" (0.000510)
195. feature "C8orf33" (0.000507)
196. feature "TPX2" (0.000507)
197. feature "PRPF40A" (0.000507)
198. feature "ENAH" (0.000506)
199. feature "AP1G1" (0.000498)
200. feature "MGRN1" (0.000493)
201. feature "G6PD" (0.000489)
202. feature "SYTL2" (0.000488)
203. feature "ATRX" (0.000486)
204. feature "TARS1" (0.000481)
205. feature "METRN" (0.000479)
206. feature "HNRNPUL1" (0.000476)
207. feature "NONO" (0.000475)
208. feature "NPM1P40" (0.000474)
209. feature "RALBP1" (0.000469)
210. feature "LARP1" (0.000465)
211. feature "JAK1" (0.000463)
212. feature "KIF5B" (0.000460)
213. feature "USP8" (0.000460)
214. feature "BRIP1" (0.000456)
215. feature "UBE2K" (0.000456)
216. feature "DYNLL2" (0.000453)
217. feature "CSK" (0.000453)
218. feature "GADD45GIP1" (0.000452)
219. feature "CCNI" (0.000451)
220. feature "SYNE2" (0.000448)
221. feature "NCOR2" (0.000446)
222. feature "MT-TQ" (0.000444)
223. feature "HTATSF1" (0.000444)
224. feature "KPNA2" (0.000444)
225. feature "CACNA1A" (0.000443)
226. feature "KRT10" (0.000443)
227. feature "NRIP1" (0.000441)
228. feature "CLIP1" (0.000439)
229. feature "GPATCH4" (0.000439)
230. feature "FAM177A1" (0.000437)
231. feature "SLTM" (0.000435)
232. feature "MRPS34" (0.000435)
233. feature "CDKN3" (0.000432)
234. feature "MYH9" (0.000432)
235. feature "FAM120A" (0.000430)
236. feature "PSMC5" (0.000429)
237. feature "ANP32E" (0.000425)
238. feature "UQCC2" (0.000423)
239. feature "PSMD14" (0.000423)
240. feature "LEO1" (0.000422)
241. feature "PPIG" (0.000421)
242. feature "UGDH" (0.000417)
243. feature "ROCK1" (0.000416)
244. feature "MED13" (0.000415)
245. feature "PHACTR2" (0.000412)
246. feature "RSRC2" (0.000412)
247. feature "C6orf62" (0.000411)
248. feature "DNAJC2" (0.000408)
249. feature "SNX27" (0.000407)
250. feature "DNMT1" (0.000406)
251. feature "PTBP3" (0.000406)
252. feature "MT-ATP8" (0.000402)
253. feature "PMEPA1" (0.000402)
254. feature "ANKRD52" (0.000401)
255. feature "SSRP1" (0.000398)
256. feature "URI1" (0.000396)
257. feature "DKC1" (0.000395)
258. feature "BDP1" (0.000391)
259. feature "NFIC" (0.000390)
260. feature "BAP1" (0.000390)
261. feature "CAV1" (0.000390)
262. feature "TRIP12" (0.000389)
263. feature "SPDL1" (0.000388)
264. feature "R3HDM2" (0.000386)
265. feature "CLTB" (0.000385)
266. feature "EIF2AK1" (0.000384)
267. feature "S100A16" (0.000379)
268. feature "RNPS1" (0.000379)
269. feature "CMSS1" (0.000376)
270. feature "CENPU" (0.000375)
271. feature "SUDS3" (0.000375)
272. feature "VTI1B" (0.000375)
273. feature "DDX21" (0.000375)
274. feature "HCFC1" (0.000373)
275. feature "PLBD2" (0.000370)
276. feature "KCNQ1OT1" (0.000370)
277. feature "PLEC" (0.000368)
278. feature "NIPBL" (0.000368)
279. feature "ITPRID2" (0.000367)
280. feature "MGP" (0.000366)
281. feature "SENP6" (0.000363)
282. feature "TRIM44" (0.000363)
283. feature "SPRY1" (0.000362)
284. feature "SUMO3" (0.000362)
285. feature "UQCC3" (0.000361)
286. feature "ZNF302" (0.000361)
287. feature "HNRNPH3" (0.000360)
288. feature "KPNA4" (0.000360)
289. feature "CENPB" (0.000359)
290. feature "STARD10" (0.000358)
291. feature "ZMYND11" (0.000358)
292. feature "CNBP" (0.000358)
293. feature "TAOK3" (0.000357)
294. feature "SEPTIN11" (0.000355)
295. feature "FBP1" (0.000355)
296. feature "ZC3H13" (0.000354)
297. feature "ATMIN" (0.000354)
298. feature "MYO5C" (0.000353)
299. feature "USP1" (0.000353)
300. feature "REST" (0.000352)
301. feature "ILF3" (0.000351)
302. feature "CDK2AP1" (0.000347)
303. feature "RTF1" (0.000346)
304. feature "ARID4B" (0.000346)
305. feature "SUPT6H" (0.000345)
306. feature "FAM102A" (0.000341)
307. feature "CDC37" (0.000340)
308. feature "ROCK2" (0.000340)
309. feature "HSPA4" (0.000340)
310. feature "DNAJC21" (0.000339)
311. feature "RAB13" (0.000338)
312. feature "ZBTB20" (0.000337)
313. feature "PHF3" (0.000337)
314. feature "PPP2R5E" (0.000337)
315. feature "GSE1" (0.000336)
316. feature "TSPYL1" (0.000335)
317. feature "ZFP36L1" (0.000332)
318. feature "MT-TS1" (0.000331)
319. feature "SLK" (0.000330)
320. feature "PRKACA" (0.000326)
321. feature "PHLDA2" (0.000325)
322. feature "SPAG9" (0.000321)
323. feature "PRMT2" (0.000321)
324. feature "STARD7" (0.000320)
325. feature "NDUFAF8" (0.000320)
326. feature "CEP55" (0.000319)
327. feature "EEA1" (0.000319)
328. feature "WSB2" (0.000319)
329. feature "BOD1L1" (0.000317)
330. feature "CDC42BPB" (0.000313)
331. feature "RRP15" (0.000311)
332. feature "GCC2" (0.000311)
333. feature "MIR663AHG" (0.000310)
334. feature "RBBP6" (0.000309)
335. feature "TNRC6A" (0.000307)
336. feature "CAMSAP2" (0.000304)
337. feature "HMGA1" (0.000303)
338. feature "RCC2" (0.000301)
339. feature "RABEP1" (0.000300)
340. feature "CHMP4B" (0.000299)
341. feature "EIF4E" (0.000299)
342. feature "NAA50" (0.000299)
343. feature "UGCG" (0.000299)
344. feature "R3HDM1" (0.000296)
345. feature "ZC3H18" (0.000296)
346. feature "MTCH1" (0.000296)
347. feature "FAM50A" (0.000296)
348. feature "INPP4B" (0.000295)
349. feature "SRGAP1" (0.000295)
350. feature "NSRP1" (0.000294)
351. feature "CENPE" (0.000294)
352. feature "UBE2Q1" (0.000292)
353. feature "ZNRF1" (0.000292)
354. feature "BCCIP" (0.000291)
355. feature "C5orf63" (0.000291)
356. feature "USP32" (0.000291)
357. feature "MDM2" (0.000289)
358. feature "IRAK1" (0.000289)
359. feature "BRD4" (0.000288)
360. feature "GARS1" (0.000288)
361. feature "TASOR2" (0.000288)
362. feature "TAF13" (0.000288)
363. feature "H2BC4" (0.000287)
364. feature "KPNA1" (0.000286)
365. feature "GATAD2A" (0.000285)
366. feature "USP7" (0.000285)
367. feature "FASN" (0.000285)
368. feature "DDX54" (0.000282)
369. feature "BRD7" (0.000282)
370. feature "CNNM2" (0.000281)
371. feature "MARCHF6" (0.000281)
372. feature "PREX1" (0.000279)
373. feature "CHD8" (0.000278)
374. feature "WWP1" (0.000276)
375. feature "MAP7" (0.000275)
376. feature "SOCS4" (0.000275)
377. feature "ZNF703" (0.000273)
378. feature "MRPL33" (0.000272)
379. feature "NAA10" (0.000272)
380. feature "APOOL" (0.000271)
381. feature "MAP3K13" (0.000271)
382. feature "ANKIB1" (0.000271)
383. feature "CASP8AP2" (0.000271)
384. feature "BTBD9" (0.000271)
385. feature "KIF1C" (0.000270)
386. feature "KCNJ2" (0.000269)
387. feature "KCNJ3" (0.000269)
388. feature "GNAQ" (0.000267)
389. feature "NEDD4L" (0.000266)
390. feature "NRBP1" (0.000265)
391. feature "SUPT5H" (0.000265)
392. feature "CNOT2" (0.000262)
393. feature "GPBP1" (0.000262)
394. feature "ARHGAP5" (0.000261)
395. feature "MDM4" (0.000260)
396. feature "TAOK1" (0.000260)
397. feature "RB1CC1" (0.000259)
398. feature "ZBTB7A" (0.000259)
399. feature "MAVS" (0.000258)
400. feature "SMG7" (0.000257)
401. feature "MTND1P23" (0.000257)
402. feature "MAGI3" (0.000256)
403. feature "PPP4R3A" (0.000256)
404. feature "FGD5-AS1" (0.000255)
405. feature "NORAD" (0.000255)
406. feature "NINJ1" (0.000253)
407. feature "KIF21A" (0.000253)
408. feature "MARK3" (0.000252)
409. feature "MAPKAPK2" (0.000250)
410. feature "WNK1" (0.000250)
411. feature "SOGA1" (0.000249)
412. feature "RBM23" (0.000249)
413. feature "REV3L" (0.000249)
414. feature "ARIH1" (0.000246)
415. feature "NET1" (0.000246)
416. feature "AP3D1" (0.000246)
417. feature "MYBL2" (0.000245)
418. feature "SMARCD2" (0.000245)
419. feature "GOLGA3" (0.000245)
420. feature "NIN" (0.000244)
421. feature "PPFIA1" (0.000242)
422. feature "RHOD" (0.000240)
423. feature "ISCU" (0.000240)
424. feature "BROX" (0.000240)
425. feature "CCDC186" (0.000239)
426. feature "RPS6KA6" (0.000238)
427. feature "LPP" (0.000238)
428. feature "ESCO1" (0.000238)
429. feature "SETD3" (0.000237)
430. feature "ATN1" (0.000237)
431. feature "RNF168" (0.000236)
432. feature "PROSER1" (0.000235)
433. feature "KMT5B" (0.000235)
434. feature "PRR34-AS1" (0.000231)
435. feature "RBBP8" (0.000231)
436. feature "SGO1" (0.000229)
437. feature "EGR1" (0.000229)
438. feature "SRM" (0.000229)
439. feature "DBF4" (0.000228)
440. feature "ZHX1" (0.000227)
441. feature "TIMELESS" (0.000226)
442. feature "SMIM15" (0.000225)
443. feature "ETF1" (0.000225)
444. feature "PRRC2A" (0.000224)
445. feature "SNHG9" (0.000223)
446. feature "PHF20L1" (0.000222)
447. feature "BTN3A2" (0.000220)
448. feature "SPIN1" (0.000220)
449. feature "SOS1" (0.000220)
450. feature "MAP3K2" (0.000219)
451. feature "IWS1" (0.000218)
452. feature "TLK2" (0.000218)
453. feature "ZMIZ1" (0.000217)
454. feature "SEPTIN9" (0.000217)
455. feature "MYBL1" (0.000217)
456. feature "NMT1" (0.000216)
457. feature "LYAR" (0.000215)
458. feature "DTYMK" (0.000215)
459. feature "BOLA3" (0.000215)
460. feature "CCP110" (0.000214)
461. feature "FNIP1" (0.000213)
462. feature "EFCAB14" (0.000213)
463. feature "CSKMT" (0.000213)
464. feature "EIF2AK2" (0.000212)
465. feature "KIF14" (0.000211)
466. feature "MYO10" (0.000210)
467. feature "PUS7" (0.000210)
468. feature "PRPF19" (0.000210)
469. feature "POLR2A" (0.000209)
470. feature "SETD2" (0.000209)
471. feature "GTF2F1" (0.000208)
472. feature "NAA15" (0.000208)
473. feature "BAZ2A" (0.000207)
474. feature "TAF3" (0.000207)
475. feature "CLSPN" (0.000206)
476. feature "ERBIN" (0.000205)
477. feature "KRT81" (0.000205)
478. feature "MRGBP" (0.000204)
479. feature "ARHGEF12" (0.000204)
480. feature "SECISBP2" (0.000204)
481. feature "MCRIP2" (0.000204)
482. feature "MAP4K4" (0.000203)
483. feature "HEPACAM" (0.000203)
484. feature "KRT80" (0.000202)
485. feature "ZNF316" (0.000202)
486. feature "ZC3H7A" (0.000201)
487. feature "LRRC59" (0.000201)
488. feature "KCTD3" (0.000201)
489. feature "BMS1" (0.000200)
490. feature "EBAG9" (0.000200)
491. feature "FARSA" (0.000199)
492. feature "IGFBP4" (0.000198)
493. feature "C2orf49" (0.000197)
494. feature "TIAL1" (0.000197)
495. feature "ZNF480" (0.000196)
496. feature "AHCYL1" (0.000195)
497. feature "EIF2B4" (0.000195)
498. feature "ETNK1" (0.000195)
499. feature "CTBP2" (0.000195)
500. feature "NSD2" (0.000194)
501. feature "CPSF2" (0.000194)
502. feature "ILRUN" (0.000194)
503. feature "PDS5A" (0.000194)
504. feature "NIBAN2" (0.000193)
505. feature "MARK2" (0.000193)
506. feature "SLC25A37" (0.000193)
507. feature "KCNK6" (0.000193)
508. feature "SART1" (0.000192)
509. feature "INCENP" (0.000192)
510. feature "CHP1" (0.000191)
511. feature "SMC6" (0.000191)
512. feature "FAM32A" (0.000191)
513. feature "NCKAP1" (0.000190)
514. feature "AP1M1" (0.000190)
515. feature "TNIP2" (0.000190)
516. feature "HIRIP3" (0.000189)
517. feature "GSR" (0.000189)
518. feature "KPNA3" (0.000189)
519. feature "TRAF3IP1" (0.000189)
520. feature "DAXX" (0.000188)
521. feature "SAPCD2" (0.000186)
522. feature "KMT2D" (0.000185)
523. feature "KLC1" (0.000185)
524. feature "PABPC4" (0.000185)
525. feature "MUC5B" (0.000185)
526. feature "CEP78" (0.000185)
527. feature "AFF4" (0.000184)
528. feature "RRP1B" (0.000184)
529. feature "FBXW5" (0.000184)
530. feature "ARID1B" (0.000183)
531. feature "CETN3" (0.000183)
532. feature "TUBD1" (0.000182)
533. feature "DCTN1" (0.000182)
534. feature "DCAF6" (0.000181)
535. feature "CTXN1" (0.000180)
536. feature "HIPK2" (0.000180)
537. feature "CFAP97" (0.000178)
538. feature "SMIM10L1" (0.000178)
539. feature "RPS19BP1" (0.000177)
540. feature "POLD2" (0.000177)
541. feature "YES1" (0.000177)
542. feature "DHX16" (0.000177)
543. feature "ANKRD17" (0.000176)
544. feature "ARFGEF1" (0.000176)
545. feature "AAMP" (0.000176)
546. feature "CBX6" (0.000176)
547. feature "HOOK1" (0.000175)
548. feature "ITPK1" (0.000175)
549. feature "GABPB2" (0.000175)
550. feature "MCL1" (0.000173)
551. feature "GALNT1" (0.000173)
552. feature "RAP1A" (0.000173)
553. feature "GDF15" (0.000173)
554. feature "OPA1" (0.000171)
555. feature "ZFC3H1" (0.000171)
556. feature "FAM199X" (0.000171)
557. feature "ALKBH5" (0.000171)
558. feature "DHTKD1" (0.000171)
559. feature "UTP18" (0.000171)
560. feature "FMR1" (0.000171)
561. feature "CA2" (0.000170)
562. feature "LRIF1" (0.000170)
563. feature "SCRIB" (0.000170)
564. feature "STX3" (0.000169)
565. feature "CDC42SE1" (0.000169)
566. feature "PRMT6" (0.000168)
567. feature "KIF3B" (0.000168)
568. feature "POGK" (0.000168)
569. feature "FAM111B" (0.000167)
570. feature "METRNL" (0.000167)
571. feature "PPM1K" (0.000167)
572. feature "DHX38" (0.000166)
573. feature "CAMK2N1" (0.000166)
574. feature "CNOT9" (0.000166)
575. feature "PLEKHB2" (0.000165)
576. feature "YAF2" (0.000165)
577. feature "NFKB1" (0.000165)
578. feature "MAPK1IP1L" (0.000164)
579. feature "OTULIN" (0.000164)
580. feature "CDK12" (0.000164)
581. feature "EIF4G3" (0.000164)
582. feature "LINC01304" (0.000163)
583. feature "ZNF146" (0.000163)
584. feature "SYCP2" (0.000163)
585. feature "CDKN1A" (0.000162)
586. feature "WIPI2" (0.000162)
587. feature "CCDC92" (0.000162)
588. feature "GOLGA5" (0.000162)
589. feature "SMC5" (0.000162)
590. feature "C1orf43" (0.000162)
591. feature "FBXW2" (0.000161)
592. feature "RBM14" (0.000161)
593. feature "NR2F6" (0.000161)
594. feature "PCYT1A" (0.000161)
595. feature "SPDEF" (0.000161)
596. feature "ATP6V1E1" (0.000160)
597. feature "LAD1" (0.000160)
598. feature "CSRP1" (0.000160)
599. feature "FKBP5" (0.000160)
600. feature "IQCE" (0.000159)
601. feature "PSME4" (0.000159)
602. feature "DNAJA2" (0.000158)
603. feature "PACS1" (0.000158)
604. feature "NOM1" (0.000157)
605. feature "ANKRD39" (0.000157)
606. feature "SCYL2" (0.000157)
607. feature "LINC01291" (0.000156)
608. feature "PCP4" (0.000156)
609. feature "CRNDE" (0.000156)
610. feature "BAG1" (0.000156)
611. feature "ELOA" (0.000156)
612. feature "OTUD7B" (0.000156)
613. feature "SENP7" (0.000156)
614. feature "NECAB1" (0.000156)
615. feature "EIF4EBP2" (0.000156)
616. feature "REV1" (0.000155)
617. feature "GBP1P1" (0.000155)
618. feature "TASOR" (0.000155)
619. feature "LRRFIP2" (0.000154)
620. feature "KHSRP" (0.000154)
621. feature "DROSHA" (0.000154)
622. feature "MT-TE" (0.000154)
623. feature "ZNF652" (0.000153)
624. feature "FBRS" (0.000153)
625. feature "SYTL4" (0.000153)
626. feature "RHNO1" (0.000153)
627. feature "UBE2J1" (0.000153)
628. feature "FUBP3" (0.000152)
629. feature "DNAJC5" (0.000152)
630. feature "ARHGAP12" (0.000152)
631. feature "OIP5-AS1" (0.000151)
632. feature "FRS2" (0.000151)
633. feature "PTGES2" (0.000150)
634. feature "MAFF" (0.000150)
635. feature "C5orf51" (0.000149)
636. feature "EPS8L2" (0.000149)
637. feature "DEDD" (0.000148)
638. feature "ANKRD26" (0.000148)
639. feature "EPB41L1" (0.000147)
640. feature "MIR99AHG" (0.000147)
641. feature "BAG3" (0.000147)
642. feature "ZYX" (0.000147)
643. feature "MYO9A" (0.000147)
644. feature "FAM83H" (0.000147)
645. feature "LCMT1" (0.000146)
646. feature "PEA15" (0.000146)
647. feature "CS" (0.000145)
648. feature "SLC25A23" (0.000145)
649. feature "FGFR1OP2" (0.000145)
650. feature "PLCB4" (0.000145)
651. feature "SUSD6" (0.000144)
652. feature "CLOCK" (0.000144)
653. feature "CDIPT" (0.000143)
654. feature "ENOX2" (0.000143)
655. feature "NUP62" (0.000143)
656. feature "POLDIP2" (0.000143)
657. feature "CBFA2T3" (0.000142)
658. feature "DDX19A" (0.000142)
659. feature "SAC3D1" (0.000142)
660. feature "TOP1MT" (0.000141)
661. feature "UTP3" (0.000141)
662. feature "FARP1" (0.000141)
663. feature "LTBR" (0.000141)
664. feature "C11orf24" (0.000140)
665. feature "CDC42BPA" (0.000140)
666. feature "KCTD20" (0.000140)
667. feature "MCM2" (0.000140)
668. feature "ZNF688" (0.000139)
669. feature "PPP2R2C" (0.000139)
670. feature "PKP3" (0.000138)
671. feature "PICALM" (0.000138)
672. feature "LCLAT1" (0.000138)
673. feature "MIR4458HG" (0.000137)
674. feature "ULK1" (0.000137)
675. feature "SLC48A1" (0.000137)
676. feature "WWC3" (0.000137)
677. feature "GYS1" (0.000137)
678. feature "LMX1B" (0.000136)
679. feature "PRKX" (0.000136)
680. feature "MPHOSPH9" (0.000136)
681. feature "HMGN5" (0.000136)
682. feature "AATF" (0.000136)
683. feature "RIPK2" (0.000136)
684. feature "FEM1A" (0.000136)
685. feature "NACC1" (0.000135)
686. feature "RP9" (0.000135)
687. feature "DHX37" (0.000135)
688. feature "PCF11" (0.000135)
689. feature "CTSL" (0.000135)
690. feature "MTMR12" (0.000135)
691. feature "ZHX3" (0.000134)
692. feature "DNMT3A" (0.000133)
693. feature "SENP5" (0.000133)
694. feature "SNX24" (0.000133)
695. feature "MGAT4B" (0.000133)
696. feature "NCBP3" (0.000133)
697. feature "ATF5" (0.000132)
698. feature "CHAF1A" (0.000132)
699. feature "RCC1L" (0.000132)
700. feature "CNDP2" (0.000132)
701. feature "PYGO2" (0.000131)
702. feature "UBE2Q2" (0.000131)
703. feature "MPP7" (0.000131)
704. feature "LINC00205" (0.000131)
705. feature "SIX4" (0.000130)
706. feature "PATJ" (0.000130)
707. feature "MSMB" (0.000130)
708. feature "PI4KB" (0.000130)
709. feature "TRIP13" (0.000130)
710. feature "KIAA1522" (0.000130)
711. feature "TNIP1" (0.000130)
712. feature "C16orf72" (0.000130)
713. feature "PARD3" (0.000129)
714. feature "PHF12" (0.000129)
715. feature "PCCA-DT" (0.000129)
716. feature "LINC01278" (0.000129)
717. feature "ARHGEF26" (0.000129)
718. feature "UBXN2B" (0.000128)
719. feature "PPP2R5A" (0.000128)
720. feature "MYB" (0.000127)
721. feature "TYW3" (0.000127)
722. feature "RAB5C" (0.000127)
723. feature "CNOT3" (0.000127)
724. feature "RARG" (0.000127)
725. feature "SINHCAFP3" (0.000127)
726. feature "ZNF326" (0.000126)
727. feature "C20orf27" (0.000126)
728. feature "LARP1B" (0.000126)
729. feature "MIEN1" (0.000126)
730. feature "MBD6" (0.000126)
731. feature "TAF9B" (0.000126)
732. feature "SLMAP" (0.000126)
733. feature "PSMD13" (0.000125)
734. feature "LIMK1" (0.000125)
735. feature "ERF" (0.000125)
736. feature "KIF16B" (0.000125)
737. feature "MAGEF1" (0.000125)
738. feature "FOXP4" (0.000124)
739. feature "PIP4K2A" (0.000124)
740. feature "ZNF252P" (0.000124)
741. feature "ZNFX1" (0.000123)
742. feature "FAM104A" (0.000123)
743. feature "THEM4" (0.000123)
744. feature "MAEA" (0.000123)
745. feature "NCOA2" (0.000123)
746. feature "H2AX" (0.000122)
747. feature "TMEM164" (0.000122)
748. feature "DNAJA3" (0.000122)
749. feature "RAB29" (0.000122)
750. feature "IFI27L2" (0.000122)
751. feature "GDI1" (0.000122)
752. feature "WDR5" (0.000121)
753. feature "PPP1R3D" (0.000121)
754. feature "EFNA5" (0.000121)
755. feature "INF2" (0.000121)
756. feature "SLC25A48" (0.000121)
757. feature "TMEM184C" (0.000120)
758. feature "PSMD5" (0.000120)
759. feature "LMNB2" (0.000120)
760. feature "CEP83" (0.000120)
761. feature "RBAK" (0.000120)
762. feature "UBP1" (0.000119)
763. feature "EP400P1" (0.000119)
764. feature "EIF1AD" (0.000119)
765. feature "RAB1B" (0.000119)
766. feature "NF2" (0.000119)
767. feature "GMFB" (0.000119)
768. feature "PALLD" (0.000118)
769. feature "DCTN6-DT" (0.000118)
770. feature "SCYL3" (0.000118)
771. feature "COG4" (0.000118)
772. feature "GMCL1" (0.000117)
773. feature "WDR82" (0.000117)
774. feature "VCPIP1" (0.000117)
775. feature "EPN1" (0.000117)
776. feature "PANK3" (0.000116)
777. feature "PALB2" (0.000116)
778. feature "RAB12" (0.000116)
779. feature "CBX4" (0.000116)
780. feature "UBE2E3" (0.000116)
781. feature "GYG1" (0.000116)
782. feature "ZNF282" (0.000115)
783. feature "CREB3L2" (0.000115)
784. feature "RAB5B" (0.000115)
785. feature "GRIPAP1" (0.000115)
786. feature "HAUS1" (0.000115)
787. feature "SNAPC5" (0.000115)
788. feature "RAD23A" (0.000115)
789. feature "VAV2" (0.000115)
790. feature "SOS2" (0.000115)
791. feature "ABL1" (0.000115)
792. feature "FBXW4" (0.000114)
793. feature "PACSIN3" (0.000114)
794. feature "ISY1" (0.000114)
795. feature "TTLL12" (0.000114)
796. feature "PCNX2" (0.000114)
797. feature "HMBS" (0.000114)
798. feature "MED1" (0.000114)
799. feature "ZNF609" (0.000113)
800. feature "FASTK" (0.000113)
801. feature "LRCH3" (0.000113)
802. feature "OPTN" (0.000113)
803. feature "SPIRE1" (0.000113)
804. feature "CEP63" (0.000113)
805. feature "CCNQ" (0.000113)
806. feature "MT-TP" (0.000113)
807. feature "RGPD4-AS1" (0.000113)
808. feature "ZMYM3" (0.000113)
809. feature "MYH14" (0.000112)
810. feature "BLOC1S3" (0.000112)
811. feature "CHAMP1" (0.000112)
812. feature "BMPR1B" (0.000112)
813. feature "TCHP" (0.000112)
814. feature "PABPN1" (0.000111)
815. feature "ARHGAP11A" (0.000111)
816. feature "NEMP1" (0.000111)
817. feature "ARHGAP1" (0.000111)
818. feature "ZBTB10" (0.000111)
819. feature "HOMER3" (0.000110)
820. feature "CRK" (0.000110)
821. feature "GLE1" (0.000110)
822. feature "NELFB" (0.000110)
823. feature "POLE" (0.000110)
824. feature "HCG18" (0.000109)
825. feature "VRK3" (0.000109)
826. feature "ACOT7" (0.000109)
827. feature "MT-TY" (0.000109)
828. feature "FMN1" (0.000109)
829. feature "SETD1B" (0.000108)
830. feature "ZFAND2A" (0.000108)
831. feature "RESF1" (0.000108)
832. feature "PKP4" (0.000108)
833. feature "CMPK1" (0.000108)
834. feature "RASSF3" (0.000107)
835. feature "RNF34" (0.000107)
836. feature "RFWD3" (0.000107)
837. feature "VPS4A" (0.000107)
838. feature "MARVELD3" (0.000107)
839. feature "COTL1" (0.000107)
840. feature "AGPAT2" (0.000107)
841. feature "DBNDD1" (0.000107)
842. feature "CHMP1A" (0.000107)
843. feature "DCP1A" (0.000106)
844. feature "ZNF26" (0.000106)
845. feature "HELB" (0.000106)
846. feature "UHRF1BP1" (0.000106)
847. feature "SHOX" (0.000106)
848. feature "RNF4" (0.000106)
849. feature "DCLRE1B" (0.000105)
850. feature "FAM214A" (0.000105)
851. feature "LETM1" (0.000105)
852. feature "SSH1" (0.000104)
853. feature "PET117" (0.000103)
854. feature "IRF2BPL" (0.000103)
855. feature "ZNF354A" (0.000103)
856. feature "ANKRD40" (0.000103)
857. feature "SIAH2" (0.000103)
858. feature "GDAP2" (0.000103)
859. feature "LINC02511" (0.000102)
860. feature "YTHDF1" (0.000102)
861. feature "ZNF512B" (0.000102)
862. feature "NUP98" (0.000102)
863. feature "ADGRV1" (0.000102)
864. feature "PIP4P1" (0.000102)
865. feature "PITX1" (0.000101)
866. feature "ABLIM1" (0.000101)
867. feature "SRSF1" (0.000101)
868. feature "ORAI1" (0.000100)
869. feature "USP25" (0.000100)
870. feature "RAB3D" (0.000100)
871. feature "WDR46" (0.000100)
872. feature "KDM1B" (0.000100)
873. feature "PIK3CB" (0.000100)
874. feature "PSMD3" (0.000100)
875. feature "FBXL19" (0.000100)
876. feature "TMCC1" (0.000099)
877. feature "PLEKHF2" (0.000099)
878. feature "CACNG4" (0.000099)
879. feature "HIF3A" (0.000099)
880. feature "ARL13B" (0.000099)
881. feature "ELP3" (0.000098)
882. feature "UBALD1" (0.000098)
883. feature "RHPN1" (0.000098)
884. feature "STK11" (0.000098)
885. feature "SALL4" (0.000098)
886. feature "CNOT6L" (0.000098)
887. feature "WDFY3" (0.000098)
888. feature "RPL12P38" (0.000098)
889. feature "RELA" (0.000098)
890. feature "DCTN5" (0.000098)
891. feature "FOXC1" (0.000097)
892. feature "CEP135" (0.000097)
893. feature "FAM102B" (0.000097)
894. feature "SAMD4B" (0.000097)
895. feature "MLLT6" (0.000097)
896. feature "NETO2" (0.000097)
897. feature "PITPNA" (0.000097)
898. feature "EHBP1" (0.000096)
899. feature "MFN2" (0.000096)
900. feature "EIPR1" (0.000096)
901. feature "THRB" (0.000096)
902. feature "COL12A1" (0.000096)
903. feature "HIP1" (0.000096)
904. feature "KLHL8" (0.000096)
905. feature "NGRN" (0.000096)
906. feature "PRR12" (0.000096)
907. feature "ITSN1" (0.000096)
908. feature "AKR1C2" (0.000096)
909. feature "PRDM4" (0.000096)
910. feature "DVL3" (0.000096)
911. feature "HOMER2" (0.000095)
912. feature "SDHAF2" (0.000095)
913. feature "HMGA2" (0.000095)
914. feature "ZNF526" (0.000095)
915. feature "STK25" (0.000095)
916. feature "MYEF2" (0.000094)
917. feature "RAPGEFL1" (0.000094)
918. feature "SECISBP2L" (0.000094)
919. feature "ZDHHC5" (0.000094)
920. feature "LATS2" (0.000094)
921. feature "RNASEH1" (0.000094)
922. feature "PIGS" (0.000094)
923. feature "TNFSF13B" (0.000094)
924. feature "RAI1" (0.000094)
925. feature "NSMCE4A" (0.000094)
926. feature "AKT1S1" (0.000094)
927. feature "ZWILCH" (0.000093)
928. feature "PSMF1" (0.000093)
929. feature "ZCCHC2" (0.000093)
930. feature "PAQR7" (0.000093)
931. feature "DCAF10" (0.000093)
932. feature "CDC25B" (0.000093)
933. feature "CDR2L" (0.000093)
934. feature "CIP2A" (0.000093)
935. feature "SHISA5" (0.000092)
936. feature "BCL3" (0.000092)
937. feature "FOXJ3" (0.000092)
938. feature "ZNF462" (0.000092)
939. feature "AZI2" (0.000092)
940. feature "AXL" (0.000092)
941. feature "IRX2" (0.000091)
942. feature "ZNF562" (0.000091)
943. feature "PCGF3" (0.000091)
944. feature "L2HGDH" (0.000091)
945. feature "MIPOL1" (0.000091)
946. feature "WDR26" (0.000091)
947. feature "CAMKK2" (0.000091)
948. feature "ZADH2" (0.000091)
949. feature "SSH3" (0.000091)
950. feature "SPATA33" (0.000091)
951. feature "ZDHHC7" (0.000091)
952. feature "RIC8A" (0.000091)
953. feature "MAP3K9" (0.000091)
954. feature "SAP130" (0.000090)
955. feature "BICD2" (0.000090)
956. feature "PAQR8" (0.000090)
957. feature "IFT74" (0.000090)
958. feature "PPP1R37" (0.000090)
959. feature "SNX4" (0.000090)
960. feature "MIS12" (0.000090)
961. feature "SPAG1" (0.000090)
962. feature "TMEM250" (0.000089)
963. feature "SMG5" (0.000089)
964. feature "RAD54L2" (0.000089)
965. feature "ZBTB37" (0.000089)
966. feature "ADARB1" (0.000089)
967. feature "TMPRSS4" (0.000089)
968. feature "TF" (0.000089)
969. feature "GRK6" (0.000089)
970. feature "ZDHHC6" (0.000089)
971. feature "USP6NL" (0.000089)
972. feature "BRF1" (0.000089)
973. feature "FBXL18" (0.000089)
974. feature "ZZZ3" (0.000089)
975. feature "CNTRL" (0.000088)
976. feature "SMIM27" (0.000088)
977. feature "FAM126B" (0.000088)
978. feature "CDCA4" (0.000088)
979. feature "TEX261" (0.000088)
980. feature "RGL2" (0.000088)
981. feature "SYNJ2" (0.000088)
982. feature "GDPGP1" (0.000088)
983. feature "CCDC149" (0.000088)
984. feature "CMBL" (0.000088)
985. feature "RFK" (0.000088)
986. feature "ZRANB1" (0.000087)
987. feature "OTUD4" (0.000087)
988. feature "UBL4A" (0.000087)
989. feature "PAK4" (0.000087)
990. feature "STK40" (0.000087)
991. feature "RHBDD2" (0.000087)
992. feature "PCNX3" (0.000087)
993. feature "CHERP" (0.000087)
994. feature "ZNF444" (0.000086)
995. feature "NLK" (0.000086)
996. feature "PHC3" (0.000086)
997. feature "PAPSS2" (0.000086)
998. feature "TMSB4XP4" (0.000086)
999. feature "NPLOC4" (0.000086)
1000. feature "ABCF2" (0.000086)
1001. feature "PREB" (0.000086)
1002. feature "TDRD3" (0.000085)
1003. feature "ZNF544" (0.000085)
1004. feature "DYRK1B" (0.000085)
1005. feature "LIN54" (0.000085)
1006. feature "FBXO11" (0.000085)
1007. feature "NECTIN1" (0.000085)
1008. feature "LRRCC1" (0.000085)
1009. feature "INPPL1" (0.000085)
1010. feature "MTMR2" (0.000085)
1011. feature "EPC2" (0.000085)
1012. feature "C2CD2L" (0.000085)
1013. feature "LCORL" (0.000085)
1014. feature "PLA2G12A" (0.000084)
1015. feature "HS6ST1" (0.000084)
1016. feature "ADAMTS19" (0.000084)
1017. feature "DTX3L" (0.000084)
1018. feature "GAREM1" (0.000084)
1019. feature "PAN3" (0.000084)
1020. feature "FDX2" (0.000084)
1021. feature "SLC52A2" (0.000084)
1022. feature "KLHL7" (0.000084)
1023. feature "PDE8A" (0.000084)
1024. feature "ZNF85" (0.000083)
1025. feature "KCTD15" (0.000083)
1026. feature "SRF" (0.000083)
1027. feature "ESRP2" (0.000083)
1028. feature "AKAP17A" (0.000083)
1029. feature "SLC35C2" (0.000083)
1030. feature "YARS1" (0.000083)
1031. feature "AKT2" (0.000083)
1032. feature "MTMR4" (0.000083)
1033. feature "INO80E" (0.000082)
1034. feature "VAT1" (0.000082)
1035. feature "CEP128" (0.000082)
1036. feature "AVEN" (0.000082)
1037. feature "ZBTB34" (0.000082)
1038. feature "BCAR1" (0.000082)
1039. feature "LYPLA2" (0.000082)
1040. feature "ATXN1L" (0.000082)
1041. feature "ZNF91" (0.000082)
1042. feature "KIF13A" (0.000081)
1043. feature "ZNF813" (0.000081)
1044. feature "SRFBP1" (0.000081)
1045. feature "ROGDI" (0.000081)
1046. feature "SEPTIN8" (0.000081)
1047. feature "TRAM2" (0.000081)
1048. feature "MRPS2" (0.000081)
1049. feature "BOP1" (0.000081)
1050. feature "ITGA3" (0.000081)
1051. feature "ADAT1" (0.000081)
1052. feature "CPSF4" (0.000081)
1053. feature "NDEL1" (0.000080)
1054. feature "FAM13B" (0.000080)
1055. feature "NIPSNAP3A" (0.000080)
1056. feature "FNTA" (0.000080)
1057. feature "TRMT12" (0.000080)
1058. feature "BCL2L15" (0.000080)
1059. feature "AGAP3" (0.000080)
1060. feature "FAM47E" (0.000080)
1061. feature "RAB35" (0.000080)
1062. feature "AMOTL1" (0.000080)
1063. feature "BICD1" (0.000080)
1064. feature "MAPKAPK5" (0.000079)
1065. feature "TACC2" (0.000079)
1066. feature "ARHGAP26" (0.000079)
1067. feature "CAMSAP1" (0.000079)
1068. feature "ZNF629" (0.000079)
1069. feature "UIMC1" (0.000079)
1070. feature "RECQL4" (0.000079)
1071. feature "BRPF3" (0.000079)
1072. feature "TP53INP1" (0.000079)
1073. feature "PACS2" (0.000079)
1074. feature "TARDBP" (0.000079)
1075. feature "CCNC" (0.000078)
1076. feature "NMI" (0.000078)
1077. feature "JMJD8" (0.000078)
1078. feature "RNF32" (0.000078)
1079. feature "SYT14" (0.000078)
1080. feature "CEP162" (0.000078)
1081. feature "PITPNC1" (0.000078)
1082. feature "ZNF385A" (0.000078)
1083. feature "CLK2" (0.000078)
1084. feature "CEBPB" (0.000078)
1085. feature "CAD" (0.000077)
1086. feature "MT-TD" (0.000077)
1087. feature "FAM222B" (0.000077)
1088. feature "NR1H2" (0.000077)
1089. feature "TJAP1" (0.000077)
1090. feature "MYD88" (0.000077)
1091. feature "TJP3" (0.000077)
1092. feature "PHLDA1" (0.000077)
1093. feature "CDK7" (0.000076)
1094. feature "CSNK2A2" (0.000076)
1095. feature "NAA16" (0.000076)
1096. feature "NEUROD2" (0.000076)
1097. feature "MNT" (0.000076)
1098. feature "ZNF48" (0.000076)
1099. feature "VRK2" (0.000076)
1100. feature "HPS1" (0.000076)
1101. feature "OPRM1" (0.000076)
1102. feature "MGLL" (0.000076)
1103. feature "YTHDF3" (0.000076)
1104. feature "RAP2A" (0.000076)
1105. feature "MAPK9" (0.000075)
1106. feature "CDK6" (0.000075)
1107. feature "RC3H2" (0.000075)
1108. feature "EFHD1" (0.000075)
1109. feature "RAB17" (0.000075)
1110. feature "BICDL1" (0.000075)
1111. feature "TATDN2" (0.000075)
1112. feature "TMEM69" (0.000075)
1113. feature "ZUP1" (0.000075)
1114. feature "GTF3C2" (0.000075)
1115. feature "TTLL5" (0.000075)
1116. feature "SLC25A44" (0.000074)
1117. feature "CLEC16A" (0.000074)
1118. feature "TP53BP2" (0.000074)
1119. feature "IMPA2" (0.000074)
1120. feature "XRCC2" (0.000074)
1121. feature "MASTL" (0.000074)
1122. feature "GIT1" (0.000073)
1123. feature "TMEM259" (0.000073)
1124. feature "EDEM1" (0.000073)
1125. feature "FOXM1" (0.000073)
1126. feature "PHF21A" (0.000073)
1127. feature "WDFY2" (0.000073)
1128. feature "RNF220" (0.000073)
1129. feature "TEAD1" (0.000073)
1130. feature "FGF12" (0.000073)
1131. feature "AFF1" (0.000073)
1132. feature "FNBP1P1" (0.000073)
1133. feature "PLAA" (0.000073)
1134. feature "PM20D2" (0.000073)
1135. feature "ZNF510" (0.000073)
1136. feature "ZHX2" (0.000073)
1137. feature "TCF20" (0.000072)
1138. feature "PPRC1" (0.000072)
1139. feature "KLF9" (0.000072)
1140. feature "GADD45A" (0.000072)
1141. feature "MT-TN" (0.000072)
1142. feature "MIOS" (0.000072)
1143. feature "SAMD8" (0.000072)
1144. feature "NKAPD1" (0.000072)
1145. feature "RASGEF1B" (0.000072)
1146. feature "NKIRAS2" (0.000072)
1147. feature "TET3" (0.000072)
1148. feature "TTL" (0.000071)
1149. feature "FAM189B" (0.000071)
1150. feature "IPO4" (0.000071)
1151. feature "PLEKHH3" (0.000071)
1152. feature "TPRA1" (0.000071)
1153. feature "GTPBP6" (0.000071)
1154. feature "SPIRE2" (0.000071)
1155. feature "HPCAL1" (0.000071)
1156. feature "DGKZ" (0.000071)
1157. feature "CAPN15" (0.000071)
1158. feature "LIAS" (0.000071)
1159. feature "TIAM1" (0.000071)
1160. feature "TRIB3" (0.000071)
1161. feature "AFG3L1P" (0.000071)
1162. feature "SERINC5" (0.000071)
1163. feature "B3GALT9" (0.000070)
1164. feature "NSMAF" (0.000070)
1165. feature "ARHGAP21" (0.000070)
1166. feature "TRAF3IP2" (0.000070)
1167. feature "MAP7D1" (0.000070)
1168. feature "LINC00491" (0.000070)
1169. feature "SLC4A1" (0.000070)
1170. feature "RFC5" (0.000070)
1171. feature "ERAP2" (0.000069)
1172. feature "SPRYD3" (0.000069)
1173. feature "SLC35A4" (0.000069)
1174. feature "ZSCAN18" (0.000069)
1175. feature "EFNA2" (0.000069)
1176. feature "FAM20B" (0.000069)
1177. feature "TIMM29" (0.000069)
1178. feature "SLAIN2" (0.000069)
1179. feature "IMPA1" (0.000069)
1180. feature "HSPA2" (0.000069)
1181. feature "SERPINE1" (0.000069)
1182. feature "CEP104" (0.000069)
1183. feature "TBC1D9" (0.000069)
1184. feature "ZNF620" (0.000068)
1185. feature "SPACA6" (0.000068)
1186. feature "CCDC18" (0.000068)
1187. feature "PCLO" (0.000068)
1188. feature "CCDC88C" (0.000068)
1189. feature "FRMD8" (0.000068)
1190. feature "KLHL20" (0.000068)
1191. feature "ABL2" (0.000068)
1192. feature "ATP6V1H" (0.000068)
1193. feature "PRXL2C" (0.000068)
1194. feature "ZNF605" (0.000068)
1195. feature "TSHZ2" (0.000068)
1196. feature "MYADM" (0.000068)
1197. feature "MAPK3" (0.000068)
1198. feature "SACS" (0.000068)
1199. feature "ASF1B" (0.000067)
1200. feature "SPATA20" (0.000067)
1201. feature "BTBD6" (0.000067)
1202. feature "IRGQ" (0.000067)
1203. feature "ARAF" (0.000067)
1204. feature "SRD5A1" (0.000067)
1205. feature "PLCD3" (0.000067)
1206. feature "STUB1" (0.000067)
1207. feature "RAD51" (0.000067)
1208. feature "E2F4" (0.000067)
1209. feature "DRAM2" (0.000067)
1210. feature "DUSP8" (0.000067)
1211. feature "PROSER3" (0.000067)
1212. feature "TNK2" (0.000067)
1213. feature "ZNF398" (0.000067)
1214. feature "C22orf46" (0.000067)
1215. feature "ZZEF1" (0.000066)
1216. feature "FOXK1" (0.000066)
1217. feature "UBA6-DT" (0.000066)
1218. feature "ZNF777" (0.000066)
1219. feature "ZNF347" (0.000066)
1220. feature "TTC28" (0.000066)
1221. feature "YJU2" (0.000066)
1222. feature "PLD6" (0.000066)
1223. feature "FARP2" (0.000066)
1224. feature "SRP68" (0.000066)
1225. feature "EGR3" (0.000065)
1226. feature "TNPO2" (0.000065)
1227. feature "SLC25A28" (0.000065)
1228. feature "USP45" (0.000065)
1229. feature "EPHB4" (0.000065)
1230. feature "TRAF6" (0.000065)
1231. feature "MANBAL" (0.000065)
1232. feature "CEP131" (0.000065)
1233. feature "USP24" (0.000065)
1234. feature "PARP9" (0.000065)
1235. feature "NPTN" (0.000065)
1236. feature "TIMM22" (0.000065)
1237. feature "AFAP1" (0.000065)
1238. feature "ANXA6" (0.000065)
1239. feature "ATXN2L" (0.000065)
1240. feature "SF3A2" (0.000064)
1241. feature "GREM1" (0.000064)
1242. feature "GLIS2" (0.000064)
1243. feature "PIK3CA" (0.000064)
1244. feature "UBQLN4" (0.000064)
1245. feature "MSR1" (0.000064)
1246. feature "BOK" (0.000064)
1247. feature "MYO9B" (0.000064)
1248. feature "VPS54" (0.000064)
1249. feature "MTND2P28" (0.000064)
1250. feature "NPRL3" (0.000063)
1251. feature "SGTA" (0.000063)
1252. feature "PPP1R12B" (0.000063)
1253. feature "C14orf28" (0.000063)
1254. feature "KIAA0895L" (0.000063)
1255. feature "MINK1" (0.000063)
1256. feature "SOX9-AS1" (0.000063)
1257. feature "ARHGAP17" (0.000063)
1258. feature "INTS3" (0.000063)
1259. feature "TIAM2" (0.000063)
1260. feature "SKI" (0.000063)
1261. feature "TPRN" (0.000063)
1262. feature "SMKR1" (0.000063)
1263. feature "UBE2F" (0.000062)
1264. feature "ATXN7L1" (0.000062)
1265. feature "KIAA0040" (0.000062)
1266. feature "GATC" (0.000062)
1267. feature "NCOA1" (0.000062)
1268. feature "JADE2" (0.000062)
1269. feature "CCNB3P1" (0.000062)
1270. feature "INPP5A" (0.000062)
1271. feature "PSCA" (0.000062)
1272. feature "RRP12" (0.000062)
1273. feature "RPS6KA4" (0.000062)
1274. feature "CACNB3" (0.000062)
1275. feature "ZNF580" (0.000062)
1276. feature "ANKS6" (0.000062)
1277. feature "DPP9" (0.000062)
1278. feature "RPLP0P2" (0.000062)
1279. feature "RILPL2" (0.000062)
1280. feature "KSR2" (0.000062)
1281. feature "KLLN" (0.000061)
1282. feature "ATG101" (0.000061)
1283. feature "PHF8" (0.000061)
1284. feature "SAMD12" (0.000061)
1285. feature "CDK8" (0.000061)
1286. feature "SSX2IP" (0.000061)
1287. feature "BRMS1L" (0.000061)
1288. feature "TIGAR" (0.000061)
1289. feature "MTARC1" (0.000061)
1290. feature "SIPA1L2" (0.000061)
1291. feature "FGFBP3" (0.000061)
1292. feature "DYRK2" (0.000061)
1293. feature "TCHH" (0.000061)
1294. feature "CRKL" (0.000061)
1295. feature "MAN2B2" (0.000061)
1296. feature "MT-TV" (0.000061)
1297. feature "NACC2" (0.000060)
1298. feature "RAB30" (0.000060)
1299. feature "NEUROD1" (0.000060)
1300. feature "THAP1" (0.000060)
1301. feature "LINC00173" (0.000060)
1302. feature "SLC36A2" (0.000060)
1303. feature "CASTOR2" (0.000060)
1304. feature "CES2" (0.000060)
1305. feature "PPP1R8" (0.000060)
1306. feature "BTRC" (0.000060)
1307. feature "SLC43A2" (0.000060)
1308. feature "SLC2A11" (0.000060)
1309. feature "BDH1" (0.000060)
1310. feature "SLITRK5" (0.000060)
1311. feature "CERS2" (0.000059)
1312. feature "PTPN13" (0.000059)
1313. feature "RUNX2" (0.000059)
1314. feature "ITPR3" (0.000059)
1315. feature "RPSAP48" (0.000059)
1316. feature "VPS9D1-AS1" (0.000059)
1317. feature "PRR36" (0.000059)
1318. feature "SPINDOC" (0.000059)
1319. feature "ASB6" (0.000059)
1320. feature "MNS1" (0.000059)
1321. feature "RNF122" (0.000059)
1322. feature "EPS8L1" (0.000059)
1323. feature "LAT2" (0.000059)
1324. feature "GCLC" (0.000059)
1325. feature "NBEAL2" (0.000059)
1326. feature "PROM2" (0.000058)
1327. feature "PHKG2" (0.000058)
1328. feature "NEDD1" (0.000058)
1329. feature "ABR" (0.000058)
1330. feature "PRPS1" (0.000058)
1331. feature "ZNF467" (0.000058)
1332. feature "ASB8" (0.000058)
1333. feature "ZNF764" (0.000058)
1334. feature "ZNF461" (0.000058)
1335. feature "TGFB1" (0.000058)
1336. feature "KCTD18" (0.000058)
1337. feature "TGFB2" (0.000058)
1338. feature "SMIM19" (0.000058)
1339. feature "PGAM5" (0.000058)
1340. feature "PRKAG1" (0.000058)
1341. feature "ENTPD1" (0.000058)
1342. feature "MYO18A" (0.000058)
1343. feature "SMIM1" (0.000058)
1344. feature "OXLD1" (0.000058)
1345. feature "RAI14" (0.000057)
1346. feature "CC2D1A" (0.000057)
1347. feature "OR4F17" (0.000057)
1348. feature "YJEFN3" (0.000057)
1349. feature "POLK" (0.000057)
1350. feature "TSSC4" (0.000057)
1351. feature "DBT" (0.000057)
1352. feature "GNPDA2" (0.000057)
1353. feature "MAP3K3" (0.000057)
1354. feature "FDXR" (0.000057)
1355. feature "IER5L" (0.000057)
1356. feature "EPN3" (0.000057)
1357. feature "SLC25A25-AS1" (0.000057)
1358. feature "ZMAT3" (0.000057)
1359. feature "RBM38" (0.000057)
1360. feature "CDH6" (0.000056)
1361. feature "ACKR3" (0.000056)
1362. feature "BAIAP2-DT" (0.000056)
1363. feature "JPH1" (0.000056)
1364. feature "MIEF1" (0.000056)
1365. feature "ONECUT2" (0.000056)
1366. feature "AGO1" (0.000056)
1367. feature "MBNL3" (0.000056)
1368. feature "KBTBD6" (0.000056)
1369. feature "SLC26A4-AS1" (0.000056)
1370. feature "HGH1" (0.000056)
1371. feature "DIS3L" (0.000056)
1372. feature "TRERF1" (0.000056)
1373. feature "USP38" (0.000056)
1374. feature "KCNJ15" (0.000056)
1375. feature "CRAMP1" (0.000056)
1376. feature "RETSAT" (0.000055)
1377. feature "UBE2D4" (0.000055)
1378. feature "LUZP1" (0.000055)
1379. feature "MGAT4A" (0.000055)
1380. feature "METTL22" (0.000055)
1381. feature "PDE12" (0.000055)
1382. feature "ELP1" (0.000055)
1383. feature "AKAP5" (0.000055)
1384. feature "LCA5L" (0.000055)
1385. feature "RAB27A" (0.000055)
1386. feature "NFX1" (0.000055)
1387. feature "DUS4L" (0.000055)
1388. feature "PML" (0.000055)
1389. feature "TPGS1" (0.000055)
1390. feature "ZNF731P" (0.000055)
1391. feature "QRSL1" (0.000055)
1392. feature "DDIAS" (0.000055)
1393. feature "MDM1" (0.000055)
1394. feature "CIAO3" (0.000055)
1395. feature "KIAA1549" (0.000055)
1396. feature "TRAK2" (0.000055)
1397. feature "TRIM11" (0.000055)
1398. feature "WDR73" (0.000054)
1399. feature "PHACTR1" (0.000054)
1400. feature "PLCB1" (0.000054)
1401. feature "WWC1" (0.000054)
1402. feature "EPAS1" (0.000054)
1403. feature "PSMG3-AS1" (0.000054)
1404. feature "ERVK13-1" (0.000054)
1405. feature "ZNF362" (0.000054)
1406. feature "L3MBTL2-AS1" (0.000054)
1407. feature "CCDC7" (0.000054)
1408. feature "ZNF768" (0.000054)
1409. feature "TFE3" (0.000054)
1410. feature "SEMA4C" (0.000054)
1411. feature "NHS" (0.000053)
1412. feature "FAAP100" (0.000053)
1413. feature "LDLRAP1" (0.000053)
1414. feature "SLC25A51" (0.000053)
1415. feature "CAMSAP3" (0.000053)
1416. feature "SOX13" (0.000053)
1417. feature "CTPS2" (0.000053)
1418. feature "GPATCH1" (0.000053)
1419. feature "MRPL20-AS1" (0.000053)
1420. feature "ANKH" (0.000053)
1421. feature "PKMYT1" (0.000053)
1422. feature "GOLT1A" (0.000053)
1423. feature "LNCARSR" (0.000053)
1424. feature "KMT2B" (0.000053)
1425. feature "ZKSCAN5" (0.000053)
1426. feature "LSM10" (0.000053)
1427. feature "CCM2" (0.000053)
1428. feature "FOXJ2" (0.000053)
1429. feature "SYNGAP1" (0.000052)
1430. feature "DLG5" (0.000052)
1431. feature "TSPAN17" (0.000052)
1432. feature "ZBTB2" (0.000052)
1433. feature "TBC1D10A" (0.000052)
1434. feature "DONSON" (0.000052)
1435. feature "KRT17" (0.000052)
1436. feature "AK7" (0.000052)
1437. feature "MED25" (0.000052)
1438. feature "FRMD3" (0.000052)
1439. feature "TAF6" (0.000052)
1440. feature "TTC7A" (0.000052)
1441. feature "PNMA1" (0.000052)
1442. feature "DENND1B" (0.000052)
1443. feature "SLC13A5" (0.000052)
1444. feature "C16orf91" (0.000052)
1445. feature "FAM193B" (0.000052)
1446. feature "PINLYP" (0.000052)
1447. feature "EYA3" (0.000052)
1448. feature "LAT" (0.000052)
1449. feature "ALAD" (0.000051)
1450. feature "FTO" (0.000051)
1451. feature "DPH1" (0.000051)
1452. feature "PPP4R1" (0.000051)
1453. feature "ATP1A4" (0.000051)
1454. feature "VPS16" (0.000051)
1455. feature "RNFT2" (0.000051)
1456. feature "BEND7" (0.000051)
1457. feature "PATL1" (0.000051)
1458. feature "PRKCD" (0.000051)
1459. feature "PCED1B-AS1" (0.000051)
1460. feature "CEP170B" (0.000051)
1461. feature "HAND2-AS1" (0.000051)
1462. feature "C3orf62" (0.000051)
1463. feature "FBXO7" (0.000051)
1464. feature "CARD10" (0.000051)
1465. feature "DOCK8-AS1" (0.000051)
1466. feature "ATXN7L3" (0.000051)
1467. feature "SBF2" (0.000051)
1468. feature "NDST1" (0.000051)
1469. feature "CASKIN2" (0.000051)
1470. feature "PTAR1" (0.000051)
1471. feature "MROH1" (0.000050)
1472. feature "SAMD15" (0.000050)
1473. feature "ZMIZ2" (0.000050)
1474. feature "SOX11" (0.000050)
1475. feature "GUCD1" (0.000050)
1476. feature "CNPPD1" (0.000050)
1477. feature "RRN3P3" (0.000050)
1478. feature "LIN52" (0.000050)
1479. feature "RNF130" (0.000050)
1480. feature "NOL4L" (0.000050)
1481. feature "PGAP6" (0.000050)
1482. feature "CAMK2D" (0.000050)
1483. feature "DPF1" (0.000049)
1484. feature "GPR158" (0.000049)
1485. feature "ZNF154" (0.000049)
1486. feature "KRT86" (0.000049)
1487. feature "FAR1" (0.000049)
1488. feature "C11orf71" (0.000049)
1489. feature "FLOT2" (0.000049)
1490. feature "TEDC2-AS1" (0.000049)
1491. feature "TBC1D13" (0.000049)
1492. feature "ZFP36" (0.000049)
1493. feature "LINC02822" (0.000049)
1494. feature "LSS" (0.000049)
1495. feature "HOXC13" (0.000049)
1496. feature "MAILR" (0.000049)
1497. feature "NEB" (0.000049)
1498. feature "OR7E13P" (0.000049)
1499. feature "RBMS3-AS3" (0.000049)
1500. feature "PXYLP1" (0.000049)
1501. feature "ZFAND2B" (0.000049)
1502. feature "KLC2" (0.000049)
1503. feature "CRYL1" (0.000049)
1504. feature "HID1" (0.000049)
1505. feature "FBXL20" (0.000049)
1506. feature "KDF1" (0.000049)
1507. feature "BACH2" (0.000048)
1508. feature "HLCS" (0.000048)
1509. feature "EVPL" (0.000048)
1510. feature "PABPC1L" (0.000048)
1511. feature "ZNF574" (0.000048)
1512. feature "ZNF142" (0.000048)
1513. feature "HES6" (0.000048)
1514. feature "POLR3E" (0.000048)
1515. feature "TRIM14" (0.000048)
1516. feature "SH2B1" (0.000048)
1517. feature "CLUH" (0.000048)
1518. feature "B9D1" (0.000048)
1519. feature "FBRSL1" (0.000048)
1520. feature "IRX4" (0.000048)
1521. feature "MARK4" (0.000048)
1522. feature "COA7" (0.000048)
1523. feature "RAB39B" (0.000048)
1524. feature "BMP1" (0.000048)
1525. feature "BMS1P23" (0.000047)
1526. feature "RGP1" (0.000047)
1527. feature "DDAH1" (0.000047)
1528. feature "ODF2L" (0.000047)
1529. feature "FAM171A2" (0.000047)
1530. feature "TMEM178B" (0.000047)
1531. feature "TMEM184B" (0.000047)
1532. feature "FAM131A" (0.000047)
1533. feature "JADE3" (0.000047)
1534. feature "PTPDC1" (0.000047)
1535. feature "PTPN9" (0.000047)
1536. feature "MOB3A" (0.000047)
1537. feature "PTPN23" (0.000047)
1538. feature "TP53I11" (0.000047)
1539. feature "ZNF483" (0.000046)
1540. feature "DEAF1" (0.000046)
1541. feature "FIZ1" (0.000046)
1542. feature "ZNF254" (0.000046)
1543. feature "VWA1" (0.000046)
1544. feature "MLX" (0.000046)
1545. feature "RIPOR1" (0.000046)
1546. feature "FOXK2" (0.000046)
1547. feature "SOCS2" (0.000046)
1548. feature "UBE2QL1" (0.000046)
1549. feature "ARHGEF37" (0.000046)
1550. feature "MPDZ" (0.000046)
1551. feature "MBD5" (0.000046)
1552. feature "RAB11FIP4" (0.000046)
1553. feature "MT-TA" (0.000046)
1554. feature "MUCL1" (0.000045)
1555. feature "CIPC" (0.000045)
1556. feature "FBXO42" (0.000045)
1557. feature "MOB1B" (0.000045)
1558. feature "TPH1" (0.000045)
1559. feature "PPP1R3B" (0.000045)
1560. feature "CDK4" (0.000045)
1561. feature "GOLGA2P7" (0.000045)
1562. feature "FLVCR2" (0.000045)
1563. feature "C7orf26" (0.000045)
1564. feature "KAZN" (0.000045)
1565. feature "MTFR1L" (0.000045)
1566. feature "ZNF35" (0.000045)
1567. feature "MMP25-AS1" (0.000045)
1568. feature "PARP6" (0.000045)
1569. feature "COPG2IT1" (0.000045)
1570. feature "NAB1" (0.000045)
1571. feature "DPY19L1P1" (0.000045)
1572. feature "TTC39A" (0.000045)
1573. feature "ZNF702P" (0.000045)
1574. feature "PCDHB9" (0.000044)
1575. feature "FADS3" (0.000044)
1576. feature "TP53INP2" (0.000044)
1577. feature "DCAF4" (0.000044)
1578. feature "STX2" (0.000044)
1579. feature "WDR62" (0.000044)
1580. feature "ACVR1" (0.000044)
1581. feature "TPP1" (0.000044)
1582. feature "PPP2R5D" (0.000044)
1583. feature "ARFGAP1" (0.000044)
1584. feature "ASB13" (0.000044)
1585. feature "ACER3" (0.000044)
1586. feature "PIK3R2" (0.000044)
1587. feature "ARSA" (0.000043)
1588. feature "SCLY" (0.000043)
1589. feature "IQSEC2" (0.000043)
1590. feature "LACTB2-AS1" (0.000043)
1591. feature "DKK1" (0.000043)
1592. feature "SNHG17" (0.000043)
1593. feature "ZNF879" (0.000043)
1594. feature "PYCR3" (0.000043)
1595. feature "WNT7B" (0.000043)
1596. feature "FBXL16" (0.000043)
1597. feature "SH3RF1" (0.000043)
1598. feature "F8" (0.000043)
1599. feature "BAGE2" (0.000043)
1600. feature "LINC00326" (0.000043)
1601. feature "NCOA5" (0.000043)
1602. feature "TPCN1" (0.000043)
1603. feature "GMEB1" (0.000043)
1604. feature "TGFB3" (0.000042)
1605. feature "MLYCD" (0.000042)
1606. feature "COL5A1" (0.000042)
1607. feature "NXF1" (0.000042)
1608. feature "TEPSIN" (0.000042)
1609. feature "C2CD5" (0.000042)
1610. feature "CYP27C1" (0.000042)
1611. feature "LYPD1" (0.000042)
1612. feature "ANKRD42" (0.000042)
1613. feature "CALML5" (0.000042)
1614. feature "SRC" (0.000042)
1615. feature "TBL3" (0.000042)
1616. feature "WT1-AS" (0.000042)
1617. feature "HDAC7" (0.000042)
1618. feature "METTL21A" (0.000041)
1619. feature "IGHMBP2" (0.000041)
1620. feature "FBXO46" (0.000041)
1621. feature "PPP1R9B" (0.000041)
1622. feature "ZBED6" (0.000041)
1623. feature "ZCCHC14" (0.000041)
1624. feature "NOTCH2NLC" (0.000041)
1625. feature "TRIM41" (0.000041)
1626. feature "STK39" (0.000041)
1627. feature "RIC1" (0.000041)
1628. feature "NXNL2" (0.000041)
1629. feature "OXNAD1" (0.000041)
1630. feature "TRAF3" (0.000041)
1631. feature "ZBTB42" (0.000041)
1632. feature "C8orf82" (0.000041)
1633. feature "ARHGEF11" (0.000041)
1634. feature "L3MBTL3" (0.000041)
1635. feature "UNC50" (0.000040)
1636. feature "DOHH" (0.000040)
1637. feature "WDR24" (0.000040)
1638. feature "SYT12" (0.000040)
1639. feature "ZNF704" (0.000040)
1640. feature "GATA2" (0.000040)
1641. feature "SP2" (0.000040)
1642. feature "RAB3B" (0.000040)
1643. feature "TMEM94" (0.000040)
1644. feature "LINC00475" (0.000040)
1645. feature "KHNYN" (0.000040)
1646. feature "ANKRD54" (0.000039)
1647. feature "DTX3" (0.000039)
1648. feature "FRAT2" (0.000039)
1649. feature "LIPE-AS1" (0.000039)
1650. feature "MPC1-DT" (0.000039)
1651. feature "ANKRD13D" (0.000039)
1652. feature "TMEM218" (0.000039)
1653. feature "LRRC37A3" (0.000039)
1654. feature "ZNF8" (0.000039)
1655. feature "CRTC1" (0.000039)
1656. feature "NAPRT" (0.000039)
1657. feature "TRAPPC9" (0.000039)
1658. feature "SH3D19" (0.000038)
1659. feature "CEP68" (0.000038)
1660. feature "ALKBH8" (0.000038)
1661. feature "ABCD4" (0.000038)
1662. feature "PHLDB2" (0.000038)
1663. feature "MYLK-AS1" (0.000038)
1664. feature "CCNE1" (0.000038)
1665. feature "XRRA1" (0.000038)
1666. feature "FSBP" (0.000038)
1667. feature "ATP1A1-AS1" (0.000038)
1668. feature "HSF2BP" (0.000038)
1669. feature "PLIN5" (0.000038)
1670. feature "GFOD1" (0.000038)
1671. feature "KITLG" (0.000038)
1672. feature "MT-TS2" (0.000038)
1673. feature "S100A9" (0.000038)
1674. feature "BBOF1" (0.000038)
1675. feature "IPO5P1" (0.000038)
1676. feature "FBXO32" (0.000038)
1677. feature "HSD17B2" (0.000038)
1678. feature "RUNDC1" (0.000038)
1679. feature "CATSPER2" (0.000038)
1680. feature "CEP85" (0.000037)
1681. feature "ECHDC2" (0.000037)
1682. feature "PKP1" (0.000037)
1683. feature "RPUSD4" (0.000037)
1684. feature "ZNF257" (0.000037)
1685. feature "ATP6V0D1-DT" (0.000037)
1686. feature "PTPN21" (0.000037)
1687. feature "LYPD4" (0.000037)
1688. feature "S1PR2" (0.000037)
1689. feature "ZNF317" (0.000037)
1690. feature "TTYH1" (0.000037)
1691. feature "KRT13" (0.000037)
1692. feature "NLRC3" (0.000037)
1693. feature "TGM2" (0.000037)
1694. feature "R3HDM4" (0.000037)
1695. feature "LRFN4" (0.000037)
1696. feature "NOVA1" (0.000037)
1697. feature "ETAA1" (0.000037)
1698. feature "BET1L" (0.000037)
1699. feature "GCLM" (0.000036)
1700. feature "ITGB6" (0.000036)
1701. feature "HBEGF" (0.000036)
1702. feature "ARHGEF17" (0.000036)
1703. feature "BMPR1A" (0.000036)
1704. feature "CABLES1" (0.000036)
1705. feature "SNX8" (0.000036)
1706. feature "NADK" (0.000036)
1707. feature "CTNNA1-AS1" (0.000036)
1708. feature "PATZ1" (0.000036)
1709. feature "PEF1" (0.000036)
1710. feature "RCC1" (0.000036)
1711. feature "TBP" (0.000036)
1712. feature "GRAMD4P3" (0.000036)
1713. feature "PARVB" (0.000036)
1714. feature "NUDT17" (0.000036)
1715. feature "BICRAL" (0.000036)
1716. feature "TMEM63C" (0.000036)
1717. feature "CCDC69" (0.000036)
1718. feature "SNHG20" (0.000036)
1719. feature "GSDMB" (0.000036)
1720. feature "CCNY-AS1" (0.000036)
1721. feature "DDX58" (0.000036)
1722. feature "IRS3P" (0.000035)
1723. feature "SESN2" (0.000035)
1724. feature "TTYH3" (0.000035)
1725. feature "RIPOR2" (0.000035)
1726. feature "IL1RAP" (0.000035)
1727. feature "FBXO30" (0.000035)
1728. feature "DIO2" (0.000035)
1729. feature "MARCHF1" (0.000035)
1730. feature "CCNYL1" (0.000035)
1731. feature "GMIP" (0.000035)
1732. feature "EHHADH" (0.000035)
1733. feature "CLEC2D" (0.000035)
1734. feature "RAB37" (0.000035)
1735. feature "KIF18B" (0.000035)
1736. feature "CLDN15" (0.000035)
1737. feature "FCHO1" (0.000035)
1738. feature "ABCB9" (0.000035)
1739. feature "EHD1" (0.000035)
1740. feature "GRASLND" (0.000035)
1741. feature "PYY2" (0.000035)
1742. feature "DNAJC6" (0.000035)
1743. feature "ABT1" (0.000035)
1744. feature "PCDHGA10" (0.000035)
1745. feature "TBC1D4" (0.000035)
1746. feature "CLDN11" (0.000035)
1747. feature "NSMF" (0.000035)
1748. feature "SBF1" (0.000035)
1749. feature "C5" (0.000035)
1750. feature "BAHD1" (0.000035)
1751. feature "SENP3" (0.000035)
1752. feature "ZNF79" (0.000034)
1753. feature "E2F1" (0.000034)
1754. feature "MEX3D" (0.000034)
1755. feature "PCDHA10" (0.000034)
1756. feature "NAIF1" (0.000034)
1757. feature "ZBTB45" (0.000034)
1758. feature "EFCAB2" (0.000034)
1759. feature "C6orf132" (0.000034)
1760. feature "PLA2G15" (0.000034)
1761. feature "CCDC18-AS1" (0.000034)
1762. feature "MAFK" (0.000034)
1763. feature "ZNF442" (0.000034)
1764. feature "UNC119" (0.000034)
1765. feature "ZFYVE28" (0.000034)
1766. feature "SLC66A2" (0.000034)
1767. feature "LINC01232" (0.000034)
1768. feature "ERVK3-1" (0.000034)
1769. feature "GTF2IRD1" (0.000034)
1770. feature "DNAJC28" (0.000034)
1771. feature "CCDC51" (0.000034)
1772. feature "CIC" (0.000034)
1773. feature "ARNTL2" (0.000034)
1774. feature "OR2C3" (0.000034)
1775. feature "PELI1" (0.000034)
1776. feature "NAGLU" (0.000034)
1777. feature "VCAN" (0.000034)
1778. feature "MICAL2" (0.000034)
1779. feature "IFI27" (0.000034)
1780. feature "SETD9" (0.000034)
1781. feature "SVIL-AS1" (0.000034)
1782. feature "WFDC1" (0.000034)
1783. feature "ZSWIM5" (0.000034)
1784. feature "CACNB2" (0.000034)
1785. feature "RENO1" (0.000034)
1786. feature "MHENCR" (0.000034)
1787. feature "EMX2OS" (0.000033)
1788. feature "MAN1C1" (0.000033)
1789. feature "DOK7" (0.000033)
1790. feature "ZBED5-AS1" (0.000033)
1791. feature "PINK1-AS" (0.000033)
1792. feature "SCNN1A" (0.000033)
1793. feature "RABGGTB" (0.000033)
1794. feature "CNR2" (0.000033)
1795. feature "LBHD1" (0.000033)
1796. feature "KIAA0825" (0.000033)
1797. feature "LINC01488" (0.000033)
1798. feature "FBXL4" (0.000033)
1799. feature "MEGF6" (0.000033)
1800. feature "TRAPPC13" (0.000033)
1801. feature "DCLRE1A" (0.000033)
1802. feature "OR2AT4" (0.000033)
1803. feature "MTMR1" (0.000033)
1804. feature "CDKL1" (0.000033)
1805. feature "GCAT" (0.000033)
1806. feature "ST3GAL1-DT" (0.000033)
1807. feature "ZFP82" (0.000033)
1808. feature "BATF" (0.000033)
1809. feature "STK35" (0.000033)
1810. feature "ADGRF1" (0.000033)
1811. feature "CAPRIN2" (0.000033)
1812. feature "TRMT2A" (0.000033)
1813. feature "TWNK" (0.000033)
1814. feature "ZIC1" (0.000032)
1815. feature "SLC25A19" (0.000032)
1816. feature "MT-TC" (0.000032)
1817. feature "GASAL1" (0.000032)
1818. feature "FAM86EP" (0.000032)
1819. feature "NXN" (0.000032)
1820. feature "MAP2K5" (0.000032)
1821. feature "LINC01750" (0.000032)
1822. feature "ALDH2" (0.000032)
1823. feature "DOC2A" (0.000032)
1824. feature "ABCA1" (0.000032)
1825. feature "ZNF626" (0.000032)
1826. feature "WDFY3-AS2" (0.000032)
1827. feature "PLEKHN1" (0.000032)
1828. feature "DLGAP1-AS2" (0.000032)
1829. feature "TVP23C" (0.000032)
1830. feature "RALGPS1" (0.000032)
1831. feature "POLB" (0.000032)
1832. feature "BCDIN3D" (0.000032)
1833. feature "SLC27A4" (0.000032)
1834. feature "KIF26A" (0.000032)
1835. feature "FAM136BP" (0.000032)
1836. feature "DGKQ" (0.000032)
1837. feature "TRPV3" (0.000032)
1838. feature "KAT2B" (0.000032)
1839. feature "SORBS3" (0.000032)
1840. feature "TUBE1" (0.000031)
1841. feature "MUL1" (0.000031)
1842. feature "C5AR1" (0.000031)
1843. feature "ITGB8-AS1" (0.000031)
1844. feature "EDA" (0.000031)
1845. feature "HSPD1P11" (0.000031)
1846. feature "THBS3" (0.000031)
1847. feature "SNHG12" (0.000031)
1848. feature "PAPOLA-DT" (0.000031)
1849. feature "DNAJA4" (0.000031)
1850. feature "OR5AS1" (0.000031)
1851. feature "KRTAP5-AS1" (0.000031)
1852. feature "UST" (0.000031)
1853. feature "SRCAP" (0.000031)
1854. feature "ZNF329" (0.000031)
1855. feature "RNF44" (0.000031)
1856. feature "TSHZ1" (0.000031)
1857. feature "AGAP2-AS1" (0.000031)
1858. feature "CROCCP3" (0.000031)
1859. feature "KANK1" (0.000031)
1860. feature "TSPAN33" (0.000031)
1861. feature "B4GALT1-AS1" (0.000031)
1862. feature "IKBKG" (0.000031)
1863. feature "TPCN2" (0.000031)
1864. feature "ZNF304" (0.000031)
1865. feature "TRPT1" (0.000031)
1866. feature "ZNF384" (0.000031)
1867. feature "CXADR" (0.000031)
1868. feature "VPS52" (0.000031)
1869. feature "CPEB4" (0.000031)
1870. feature "WDR72" (0.000030)
1871. feature "RAB40C" (0.000030)
1872. feature "SKIDA1" (0.000030)
1873. feature "GTPBP2" (0.000030)
1874. feature "LRRC69" (0.000030)
1875. feature "HDHD5-AS1" (0.000030)
1876. feature "ARVCF" (0.000030)
1877. feature "AMH" (0.000030)
1878. feature "DNAAF11" (0.000030)
1879. feature "SULT1B1" (0.000030)
1880. feature "HELZ2" (0.000030)
1881. feature "PURPL" (0.000030)
1882. feature "RPL36AP26" (0.000030)
1883. feature "OAZ3" (0.000030)
1884. feature "B3GAT2" (0.000030)
1885. feature "CALML3-AS1" (0.000030)
1886. feature "ALDH3B2" (0.000030)
1887. feature "LIPT1" (0.000030)
1888. feature "MTF1" (0.000030)
1889. feature "SOWAHC" (0.000030)
1890. feature "TM4SF19-AS1" (0.000030)
1891. feature "GPATCH3" (0.000030)
1892. feature "ILDR1" (0.000030)
1893. feature "MAD2L1BP" (0.000030)
1894. feature "LRRC73" (0.000030)
1895. feature "STARD3" (0.000030)
1896. feature "C2CD2" (0.000030)
1897. feature "CCDC88B" (0.000030)
1898. feature "CRTC2" (0.000030)
1899. feature "PYCR1" (0.000030)
1900. feature "TSBP1-AS1" (0.000030)
1901. feature "LINC00639" (0.000030)
1902. feature "STAM-DT" (0.000029)
1903. feature "NCK2" (0.000029)
1904. feature "RNF146" (0.000029)
1905. feature "UCKL1" (0.000029)
1906. feature "ST6GAL1" (0.000029)
1907. feature "EDA2R" (0.000029)
1908. feature "HR" (0.000029)
1909. feature "DOLPP1" (0.000029)
1910. feature "PCGF2" (0.000029)
1911. feature "HMGB1P39" (0.000029)
1912. feature "STRIP1" (0.000029)
1913. feature "PPP2R3B" (0.000029)
1914. feature "FRAT1" (0.000029)
1915. feature "CNIH3" (0.000029)
1916. feature "TENT5C" (0.000029)
1917. feature "TMEM212" (0.000029)
1918. feature "NTN1" (0.000029)
1919. feature "PEX7" (0.000029)
1920. feature "IL4R" (0.000029)
1921. feature "RBM15B" (0.000029)
1922. feature "RPS12P16" (0.000029)
1923. feature "SERPINB9P1" (0.000029)
1924. feature "TRIM3" (0.000029)
1925. feature "SLC23A2" (0.000029)
1926. feature "TRAF4" (0.000029)
1927. feature "CCND3" (0.000029)
1928. feature "SNHG30" (0.000029)
1929. feature "ARRDC2" (0.000029)
1930. feature "FAM20C" (0.000029)
1931. feature "BORCS6" (0.000029)
1932. feature "AFAP1L2" (0.000029)
1933. feature "RAET1E" (0.000029)
1934. feature "ZDHHC18" (0.000029)
1935. feature "ELOVL7" (0.000029)
1936. feature "SPSB1" (0.000029)
1937. feature "CAPN13" (0.000028)
1938. feature "ZNF696" (0.000028)
1939. feature "GPRIN1" (0.000028)
1940. feature "RBM24" (0.000028)
1941. feature "DIABLO" (0.000028)
1942. feature "OR7E25P" (0.000028)
1943. feature "LIX1L" (0.000028)
1944. feature "PCDHB1" (0.000028)
1945. feature "MAPK8IP2" (0.000028)
1946. feature "MT1F" (0.000028)
1947. feature "ACTG2" (0.000028)
1948. feature "ZNF383" (0.000028)
1949. feature "MRPL38" (0.000028)
1950. feature "NCAM1" (0.000028)
1951. feature "SLC25A43" (0.000028)
1952. feature "BRME1" (0.000028)
1953. feature "NCDN" (0.000028)
1954. feature "TMEM167B-DT" (0.000028)
1955. feature "KIFC2" (0.000028)
1956. feature "SCARA3" (0.000028)
1957. feature "DDX28" (0.000028)
1958. feature "MRTFA" (0.000028)
1959. feature "KIZ" (0.000028)
1960. feature "FAM241B" (0.000028)
1961. feature "FAM219B" (0.000028)
1962. feature "LYPD6" (0.000028)
1963. feature "ZNF165" (0.000028)
1964. feature "PELI3" (0.000028)
1965. feature "FBXO27" (0.000028)
1966. feature "FHIP1A" (0.000028)
1967. feature "FZD1" (0.000028)
1968. feature "AUNIP" (0.000028)
1969. feature "SLC19A1" (0.000028)
1970. feature "RRAGC" (0.000027)
1971. feature "PPP3CC" (0.000027)
1972. feature "ANKRD20A5P" (0.000027)
1973. feature "MMP15" (0.000027)
1974. feature "MAFG" (0.000027)
1975. feature "CRYBG2" (0.000027)
1976. feature "CCDC144A" (0.000027)
1977. feature "LINC01234" (0.000027)
1978. feature "STAT5B" (0.000027)
1979. feature "ZNF219" (0.000027)
1980. feature "CDK9" (0.000027)
1981. feature "PC" (0.000027)
1982. feature "ABHD15" (0.000027)
1983. feature "STON2" (0.000027)
1984. feature "MID2" (0.000027)
1985. feature "TAF8" (0.000027)
1986. feature "RBFOX3" (0.000027)
1987. feature "U2AF1L4" (0.000027)
1988. feature "SNORD3B-1" (0.000027)
1989. feature "PLPPR2" (0.000027)
1990. feature "ZNF449" (0.000027)
1991. feature "BCAM" (0.000027)
1992. feature "POTEC" (0.000027)
1993. feature "ZNF628" (0.000027)
1994. feature "TBC1D22B" (0.000027)
1995. feature "NRARP" (0.000026)
1996. feature "MAP3K14-AS1" (0.000026)
1997. feature "ZKSCAN8" (0.000026)
1998. feature "ADCK1" (0.000026)
1999. feature "ELMOD3" (0.000026)
2000. feature "RASSF8" (0.000026)
2001. feature "PABIR2" (0.000026)
2002. feature "HECA" (0.000026)
2003. feature "MARS2" (0.000026)
2004. feature "FAM110D" (0.000026)
2005. feature "SLC35C1" (0.000026)
2006. feature "TRIM39" (0.000026)
2007. feature "ZBTB26" (0.000026)
2008. feature "PCDHB4" (0.000026)
2009. feature "TRIT1" (0.000026)
2010. feature "BDNF-AS" (0.000026)
2011. feature "TMEM115" (0.000026)
2012. feature "VPS18" (0.000026)
2013. feature "C8orf58" (0.000026)
2014. feature "GCAWKR" (0.000026)
2015. feature "PLEKHG6" (0.000026)
2016. feature "PRR7-AS1" (0.000026)
2017. feature "OR10K1" (0.000026)
2018. feature "SCRG1" (0.000026)
2019. feature "LINC00345" (0.000026)
2020. feature "DLX1" (0.000026)
2021. feature "GASK1B-AS1" (0.000026)
2022. feature "SNAP25-AS1" (0.000026)
2023. feature "N6AMT1" (0.000026)
2024. feature "ZNF354B" (0.000026)
2025. feature "FHIP1B" (0.000026)
2026. feature "MTATP6P1" (0.000026)
2027. feature "RRP9" (0.000026)
2028. feature "TWF2" (0.000026)
2029. feature "LINC01918" (0.000025)
2030. feature "TEAD2" (0.000025)
2031. feature "PCSK5" (0.000025)
2032. feature "UBIAD1" (0.000025)
2033. feature "ZNF77" (0.000025)
2034. feature "KPNB1P1" (0.000025)
2035. feature "PPARA" (0.000025)
2036. feature "ITGB8" (0.000025)
2037. feature "BCR" (0.000025)
2038. feature "ZNF524" (0.000025)
2039. feature "SALRNA1" (0.000025)
2040. feature "GAB2" (0.000025)
2041. feature "POMK" (0.000025)
2042. feature "ADAP1" (0.000025)
2043. feature "NANP" (0.000025)
2044. feature "MYO1F" (0.000025)
2045. feature "HCG11" (0.000025)
2046. feature "PADI2" (0.000025)
2047. feature "PIP5K1C" (0.000025)
2048. feature "RSAD1" (0.000025)
2049. feature "LINC01206" (0.000025)
2050. feature "TADA2B" (0.000025)
2051. feature "PARM1" (0.000025)
2052. feature "IL1R1" (0.000025)
2053. feature "THRA" (0.000025)
2054. feature "NOP2" (0.000025)
2055. feature "CDKN2B" (0.000025)
2056. feature "CNPY4" (0.000025)
2057. feature "PRPF40B" (0.000025)
2058. feature "BFSP1" (0.000025)
2059. feature "METTL14-DT" (0.000025)
2060. feature "OLFM2" (0.000025)
2061. feature "RHD" (0.000025)
2062. feature "OSBP2" (0.000025)
2063. feature "AQP11" (0.000025)
2064. feature "CRB1" (0.000024)
2065. feature "CEACAM5" (0.000024)
2066. feature "CREB3L1" (0.000024)
2067. feature "TRIM8" (0.000024)
2068. feature "SH3PXD2A" (0.000024)
2069. feature "ADD2" (0.000024)
2070. feature "CABLES2" (0.000024)
2071. feature "PPP1R12A-AS1" (0.000024)
2072. feature "MFSD4A" (0.000024)
2073. feature "MMD" (0.000024)
2074. feature "ZNF572" (0.000024)
2075. feature "ARHGAP39" (0.000024)
2076. feature "CHAC2" (0.000024)
2077. feature "FUOM" (0.000024)
2078. feature "KRT16" (0.000024)
2079. feature "WFS1" (0.000024)
2080. feature "GRIN1" (0.000024)
2081. feature "LINC00343" (0.000024)
2082. feature "ZNF396" (0.000024)
2083. feature "EPCAM-DT" (0.000024)
2084. feature "INPP5E" (0.000024)
2085. feature "EFCAB5" (0.000024)
2086. feature "SNAPC2" (0.000024)
2087. feature "ZKSCAN2" (0.000024)
2088. feature "ZNF550" (0.000024)
2089. feature "CDC42EP1" (0.000024)
2090. feature "CCDC88A" (0.000024)
2091. feature "HECTD2" (0.000024)
2092. feature "TBXA2R" (0.000024)
2093. feature "POC1A" (0.000024)
2094. feature "CCDC183" (0.000024)
2095. feature "ANKRD45" (0.000024)
2096. feature "TRIM68" (0.000024)
2097. feature "RHOU" (0.000024)
2098. feature "ZBED6CL" (0.000024)
2099. feature "SIGMAR1" (0.000024)
2100. feature "TNNC2" (0.000024)
2101. feature "ABHD1" (0.000024)
2102. feature "ZNF202" (0.000024)
2103. feature "CDADC1" (0.000023)
2104. feature "SLC2A13" (0.000023)
2105. feature "FAM185BP" (0.000023)
2106. feature "RHOH" (0.000023)
2107. feature "MIB2" (0.000023)
2108. feature "ESS2" (0.000023)
2109. feature "CASP10" (0.000023)
2110. feature "LY6D" (0.000023)
2111. feature "IGSF6" (0.000023)
2112. feature "LINC02482" (0.000023)
2113. feature "ZNF280B" (0.000023)
2114. feature "ARHGEF4" (0.000023)
2115. feature "PPP2R2B" (0.000023)
2116. feature "TENT2" (0.000023)
2117. feature "MAPT-AS1" (0.000023)
2118. feature "MEMO1" (0.000023)
2119. feature "PLXNA1" (0.000023)
2120. feature "HEATR3" (0.000023)
2121. feature "LINC02609" (0.000023)
2122. feature "PARP16" (0.000023)
2123. feature "TDRD5" (0.000023)
2124. feature "ZNF843" (0.000023)
2125. feature "SCX" (0.000023)
2126. feature "STK16" (0.000023)
2127. feature "TMEM201" (0.000023)
2128. feature "PLAC9P1" (0.000023)
2129. feature "WASH3P" (0.000023)
2130. feature "REM2" (0.000023)
2131. feature "MKLN1-AS" (0.000023)
2132. feature "CCDC120" (0.000023)
2133. feature "LINC01128" (0.000023)
2134. feature "GPR137C" (0.000023)
2135. feature "DISP1" (0.000023)
2136. feature "ADAMTSL5" (0.000023)
2137. feature "ZNF180" (0.000022)
2138. feature "IFITM3P6" (0.000022)
2139. feature "TCEANC" (0.000022)
2140. feature "ARMC6" (0.000022)
2141. feature "CCNO" (0.000022)
2142. feature "AKT3" (0.000022)
2143. feature "SMIM5" (0.000022)
2144. feature "ABHD16A" (0.000022)
2145. feature "LINC02241" (0.000022)
2146. feature "NLE1" (0.000022)
2147. feature "WRAP73" (0.000022)
2148. feature "LINC01962" (0.000022)
2149. feature "ABCD1" (0.000022)
2150. feature "LINC01725" (0.000022)
2151. feature "GALM" (0.000022)
2152. feature "STRIP2" (0.000022)
2153. feature "ZNF653" (0.000022)
2154. feature "ZNF816" (0.000022)
2155. feature "LINC02762" (0.000022)
2156. feature "ARAP1" (0.000022)
2157. feature "FAM43A" (0.000022)
2158. feature "LINC00958" (0.000022)
2159. feature "MT-TM" (0.000022)
2160. feature "SLC9A3-AS1" (0.000022)
2161. feature "MTCL1" (0.000022)
2162. feature "SLC47A1" (0.000022)
2163. feature "PTDSS2" (0.000022)
2164. feature "TMPRSS3" (0.000022)
2165. feature "TMEM139" (0.000022)
2166. feature "WDR5B-DT" (0.000022)
2167. feature "CDC14A" (0.000022)
2168. feature "EPOP" (0.000022)
2169. feature "PLCD4" (0.000022)
2170. feature "ST6GALNAC4" (0.000022)
2171. feature "C6orf136" (0.000022)
2172. feature "SLC19A2" (0.000022)
2173. feature "PCAT1" (0.000022)
2174. feature "UAP1L1" (0.000022)
2175. feature "RRN3P1" (0.000022)
2176. feature "DFFB" (0.000022)
2177. feature "DDX25" (0.000022)
2178. feature "FOXP4-AS1" (0.000022)
2179. feature "VAV1" (0.000022)
2180. feature "RHOBTB2" (0.000022)
2181. feature "NEK8" (0.000022)
2182. feature "GRM6" (0.000022)
2183. feature "USF1" (0.000021)
2184. feature "GNAO1-DT" (0.000021)
2185. feature "KLHL22" (0.000021)
2186. feature "DNAJC3-DT" (0.000021)
2187. feature "PIKFYVE" (0.000021)
2188. feature "ANKRD13B" (0.000021)
2189. feature "TBKBP1" (0.000021)
2190. feature "ADCY2" (0.000021)
2191. feature "GAMT" (0.000021)
2192. feature "LHX2" (0.000021)
2193. feature "PIDD1" (0.000021)
2194. feature "GNG7" (0.000021)
2195. feature "CYB561D2" (0.000021)
2196. feature "CACNA1H" (0.000021)
2197. feature "ST20-AS1" (0.000021)
2198. feature "HYAL2" (0.000021)
2199. feature "OXSM" (0.000021)
2200. feature "NOSTRIN" (0.000021)
2201. feature "PCBP3" (0.000021)
2202. feature "GPR137B" (0.000021)
2203. feature "TANGO2" (0.000021)
2204. feature "HMGB1P8" (0.000021)
2205. feature "HECTD3" (0.000021)
2206. feature "ZBTB39" (0.000021)
2207. feature "PPP1R18" (0.000021)
2208. feature "CLCF1" (0.000021)
2209. feature "ZNF578" (0.000021)
2210. feature "TPST1" (0.000021)
2211. feature "DOCK9" (0.000021)
2212. feature "B3GALT5" (0.000021)
2213. feature "MFSD2B" (0.000021)
2214. feature "PTGES3L" (0.000021)
2215. feature "CERK" (0.000021)
2216. feature "RNF126" (0.000021)
2217. feature "ANKRD34A" (0.000021)
2218. feature "ZC3H12A" (0.000021)
2219. feature "CDAN1" (0.000020)
2220. feature "GPAT3" (0.000020)
2221. feature "RYR3" (0.000020)
2222. feature "ZNF579" (0.000020)
2223. feature "NRG1" (0.000020)
2224. feature "MPV17L2" (0.000020)
2225. feature "APOBEC3F" (0.000020)
2226. feature "HSP90AA2P" (0.000020)
2227. feature "ARL17B" (0.000020)
2228. feature "BCL2L2" (0.000020)
2229. feature "ZFP3" (0.000020)
2230. feature "DENND3" (0.000020)
2231. feature "FLT3LG" (0.000020)
2232. feature "CHRNB1" (0.000020)
2233. feature "HACD4" (0.000020)
2234. feature "TESK1" (0.000020)
2235. feature "MCOLN1" (0.000020)
2236. feature "MPP3" (0.000020)
2237. feature "GAS2L1" (0.000020)
2238. feature "KCNC3" (0.000020)
2239. feature "PRR19" (0.000020)
2240. feature "RAVER1" (0.000020)
2241. feature "C19orf73" (0.000020)
2242. feature "FBXO17" (0.000020)
2243. feature "TBC1D8" (0.000020)
2244. feature "NPFFR1" (0.000020)
2245. feature "EREG" (0.000020)
2246. feature "VASH1" (0.000020)
2247. feature "SNX25" (0.000020)
2248. feature "KIF12" (0.000020)
2249. feature "UPP1" (0.000020)
2250. feature "PITPNM2" (0.000020)
2251. feature "TNS2" (0.000020)
2252. feature "MIRLET7BHG" (0.000020)
2253. feature "GPRASP2" (0.000020)
2254. feature "ZC3H10" (0.000020)
2255. feature "GMEB2" (0.000020)
2256. feature "TNIK" (0.000020)
2257. feature "NXPE3" (0.000020)
2258. feature "TSACC" (0.000020)
2259. feature "ZNF837" (0.000020)
2260. feature "AMIGO1" (0.000020)
2261. feature "PAQR4" (0.000020)
2262. feature "ZNF433" (0.000020)
2263. feature "EPM2A" (0.000020)
2264. feature "FMNL3" (0.000019)
2265. feature "XKR6" (0.000019)
2266. feature "TMEM222" (0.000019)
2267. feature "FAM81A" (0.000019)
2268. feature "LINC01551" (0.000019)
2269. feature "PECR" (0.000019)
2270. feature "ARHGEF34P" (0.000019)
2271. feature "PYCR2" (0.000019)
2272. feature "TMX3" (0.000019)
2273. feature "YRDC" (0.000019)
2274. feature "PFKFB2" (0.000019)
2275. feature "B4GAT1" (0.000019)
2276. feature "CKMT1A" (0.000019)
2277. feature "FOSB" (0.000019)
2278. feature "TTC38" (0.000019)
2279. feature "TMEM62" (0.000019)
2280. feature "LINC00672" (0.000019)
2281. feature "MOB2" (0.000019)
2282. feature "HEXD-IT1" (0.000019)
2283. feature "MSL3P1" (0.000019)
2284. feature "EN2" (0.000019)
2285. feature "SNORC" (0.000019)
2286. feature "ZNF773" (0.000019)
2287. feature "KIAA0895" (0.000019)
2288. feature "FAM89A" (0.000019)
2289. feature "TNFAIP8L3" (0.000019)
2290. feature "DOK4" (0.000019)
2291. feature "GLMP" (0.000019)
2292. feature "GPR85" (0.000019)
2293. feature "NOG" (0.000018)
2294. feature "KLHL13" (0.000018)
2295. feature "SCN9A" (0.000018)
2296. feature "MSTO1" (0.000018)
2297. feature "DENND5B" (0.000018)
2298. feature "FAM72D" (0.000018)
2299. feature "PAX9" (0.000018)
2300. feature "MMP16" (0.000018)
2301. feature "SRRD" (0.000018)
2302. feature "SLC36A4" (0.000018)
2303. feature "RPL37P6" (0.000018)
2304. feature "NFKBID" (0.000018)
2305. feature "ARHGEF28" (0.000018)
2306. feature "SMPD2" (0.000018)
2307. feature "SPRED3" (0.000018)
2308. feature "TXNRD2" (0.000018)
2309. feature "SPATA6L" (0.000018)
2310. feature "SIX3" (0.000018)
2311. feature "CDKN2D" (0.000018)
2312. feature "CACNB4" (0.000018)
2313. feature "E2F2" (0.000018)
2314. feature "KLK6" (0.000018)
2315. feature "C12orf56" (0.000018)
2316. feature "SLC41A3-AS1" (0.000018)
2317. feature "IPPK" (0.000018)
2318. feature "CCDC81" (0.000018)
2319. feature "L3HYPDH" (0.000018)
2320. feature "FAM71D" (0.000018)
2321. feature "PARD3B" (0.000018)
2322. feature "MAPK12" (0.000018)
2323. feature "LPAR6" (0.000018)
2324. feature "SLC30A1" (0.000018)
2325. feature "NOTCH4" (0.000018)
2326. feature "TMPRSS2" (0.000018)
2327. feature "ADAP2" (0.000018)
2328. feature "JHY" (0.000018)
2329. feature "TRMT61A" (0.000018)
2330. feature "ATRNL1" (0.000018)
2331. feature "PER1" (0.000018)
2332. feature "RUNDC3A" (0.000018)
2333. feature "CERS4" (0.000018)
2334. feature "KATNB1" (0.000018)
2335. feature "GPN1" (0.000018)
2336. feature "SNORA11F" (0.000018)
2337. feature "EDC4" (0.000018)
2338. feature "PARP12" (0.000018)
2339. feature "ADM2" (0.000018)
2340. feature "PTHLH" (0.000018)
2341. feature "ATP2A1-AS1" (0.000018)
2342. feature "TFEB" (0.000018)
2343. feature "ZNF699" (0.000018)
2344. feature "S100A8" (0.000018)
2345. feature "DOCK11" (0.000018)
2346. feature "NR2F1-AS1" (0.000018)
2347. feature "ATG9A" (0.000018)
2348. feature "A4GALT" (0.000017)
2349. feature "CLYBL" (0.000017)
2350. feature "EML5" (0.000017)
2351. feature "RTL10" (0.000017)
2352. feature "MON1A" (0.000017)
2353. feature "MPP2" (0.000017)
2354. feature "ARMCX4" (0.000017)
2355. feature "MSX2" (0.000017)
2356. feature "KCNJ6" (0.000017)
2357. feature "KLK10" (0.000017)
2358. feature "GABBR2" (0.000017)
2359. feature "LRRC56" (0.000017)
2360. feature "PLEKHA4" (0.000017)
2361. feature "ZNF343" (0.000017)
2362. feature "ZNF76" (0.000017)
2363. feature "VWA2" (0.000017)
2364. feature "CXCL12" (0.000017)
2365. feature "PIGB" (0.000017)
2366. feature "DLX6-AS1" (0.000017)
2367. feature "PACC1" (0.000017)
2368. feature "TMEM39A" (0.000017)
2369. feature "LINC02324" (0.000017)
2370. feature "NFATC2" (0.000017)
2371. feature "ZNF321P" (0.000017)
2372. feature "MELTF" (0.000017)
2373. feature "INPP4A" (0.000017)
2374. feature "PHETA1" (0.000017)
2375. feature "ZIC2" (0.000017)
2376. feature "BRINP2" (0.000017)
2377. feature "ZNF672" (0.000017)
2378. feature "HHLA1" (0.000017)
2379. feature "EID2" (0.000017)
2380. feature "SLC4A10" (0.000017)
2381. feature "CYP1A1" (0.000017)
2382. feature "PLEKHG5" (0.000017)
2383. feature "DOCK8" (0.000017)
2384. feature "KIF13B" (0.000017)
2385. feature "ZNF2" (0.000017)
2386. feature "HHEX" (0.000017)
2387. feature "BCL2" (0.000017)
2388. feature "ZEB1-AS1" (0.000017)
2389. feature "NFATC3" (0.000017)
2390. feature "ZFP41" (0.000017)
2391. feature "CSGALNACT1" (0.000017)
2392. feature "ADORA1" (0.000017)
2393. feature "RFPL3S" (0.000017)
2394. feature "ZNF335" (0.000017)
2395. feature "TNFRSF10B" (0.000016)
2396. feature "PRICKLE2" (0.000016)
2397. feature "FOXO6" (0.000016)
2398. feature "INMT" (0.000016)
2399. feature "LRRC37A" (0.000016)
2400. feature "ZFHX2" (0.000016)
2401. feature "RTN4RL1" (0.000016)
2402. feature "KCTD6" (0.000016)
2403. feature "ZNF722P" (0.000016)
2404. feature "MSRB3" (0.000016)
2405. feature "GARNL3" (0.000016)
2406. feature "IGDCC3" (0.000016)
2407. feature "SUSD3" (0.000016)
2408. feature "USP46-DT" (0.000016)
2409. feature "RSPH1" (0.000016)
2410. feature "HMGB1P1" (0.000016)
2411. feature "CORO2A" (0.000016)
2412. feature "ARHGEF18" (0.000016)
2413. feature "STIMATE" (0.000016)
2414. feature "HAP1" (0.000016)
2415. feature "ZIC5" (0.000016)
2416. feature "C20orf204" (0.000016)
2417. feature "ADRA2C" (0.000016)
2418. feature "TNFAIP2" (0.000016)
2419. feature "SLC66A1" (0.000016)
2420. feature "PGBD5" (0.000016)
2421. feature "ATP6V0C" (0.000016)
2422. feature "MAMLD1" (0.000016)
2423. feature "DHRS11" (0.000016)
2424. feature "FAM222A" (0.000016)
2425. feature "SPHK1" (0.000016)
2426. feature "HPS6" (0.000016)
2427. feature "TMEM51" (0.000016)
2428. feature "CSRNP1" (0.000016)
2429. feature "AK1" (0.000016)
2430. feature "LPCAT4" (0.000016)
2431. feature "PYGO1" (0.000016)
2432. feature "FA2H" (0.000016)
2433. feature "MOCS1" (0.000016)
2434. feature "MYLPF" (0.000016)
2435. feature "MYG1-AS1" (0.000016)
2436. feature "HTR7P1" (0.000016)
2437. feature "SEC14L2" (0.000015)
2438. feature "SNHG11" (0.000015)
2439. feature "SATL1" (0.000015)
2440. feature "LRP3" (0.000015)
2441. feature "ARHGEF19" (0.000015)
2442. feature "CAPN10" (0.000015)
2443. feature "SCOC-AS1" (0.000015)
2444. feature "LIFR" (0.000015)
2445. feature "SLC29A3" (0.000015)
2446. feature "PRKCH" (0.000015)
2447. feature "ZNF585B" (0.000015)
2448. feature "ARSJ" (0.000015)
2449. feature "LINC00240" (0.000015)
2450. feature "RAB23" (0.000015)
2451. feature "ABHD3" (0.000015)
2452. feature "HELQ" (0.000015)
2453. feature "ACOT2" (0.000015)
2454. feature "RBM26-AS1" (0.000015)
2455. feature "RHOBTB1" (0.000015)
2456. feature "SIM2" (0.000015)
2457. feature "HCN2" (0.000015)
2458. feature "ZNF134" (0.000015)
2459. feature "GPR132" (0.000015)
2460. feature "SLC52A3" (0.000015)
2461. feature "ROM1" (0.000015)
2462. feature "ZNF232" (0.000015)
2463. feature "HMGN1P14" (0.000015)
2464. feature "ZNF69" (0.000015)
2465. feature "PSG2" (0.000015)
2466. feature "VDR" (0.000015)
2467. feature "CRACR2B" (0.000015)
2468. feature "TLE2" (0.000015)
2469. feature "LINC00294" (0.000015)
2470. feature "CDKL3" (0.000015)
2471. feature "RTL6" (0.000014)
2472. feature "CAMTA2" (0.000014)
2473. feature "CRX" (0.000014)
2474. feature "ALDH4A1" (0.000014)
2475. feature "LINC02913" (0.000014)
2476. feature "CHN1" (0.000014)
2477. feature "HMGB1P51" (0.000014)
2478. feature "IFIT5" (0.000014)
2479. feature "CARD18" (0.000014)
2480. feature "TRAPPC14" (0.000014)
2481. feature "RILP" (0.000014)
2482. feature "ZNF767P" (0.000014)
2483. feature "DNAI4" (0.000014)
2484. feature "RNVU1-30" (0.000014)
2485. feature "INPP1" (0.000014)
2486. feature "CHRNG" (0.000014)
2487. feature "OR2L2" (0.000014)
2488. feature "RAD52" (0.000014)
2489. feature "MAP3K10" (0.000014)
2490. feature "SIGLEC15" (0.000014)
2491. feature "PMS2CL" (0.000014)
2492. feature "MICALL1" (0.000014)
2493. feature "PCDHGB6" (0.000014)
2494. feature "ZNF674" (0.000014)
2495. feature "C2orf42" (0.000014)
2496. feature "BEX5" (0.000014)
2497. feature "C12orf76" (0.000014)
2498. feature "RPIA" (0.000014)
2499. feature "SHROOM2" (0.000014)
2500. feature "PTPN7" (0.000014)
2501. feature "KYAT3" (0.000014)
2502. feature "ITM2C" (0.000014)
2503. feature "ZNF555" (0.000014)
2504. feature "DSTNP2" (0.000014)
2505. feature "CRPPA" (0.000014)
2506. feature "LINC01426" (0.000014)
2507. feature "HS3ST3B1" (0.000013)
2508. feature "ZNF441" (0.000013)
2509. feature "RNASEK" (0.000013)
2510. feature "PRORSD1P" (0.000013)
2511. feature "PA2G4P6" (0.000013)
2512. feature "RHOQP1" (0.000013)
2513. feature "ZNF324" (0.000013)
2514. feature "KRT18P3" (0.000013)
2515. feature "ARMC7" (0.000013)
2516. feature "LRFN3" (0.000013)
2517. feature "SRP14-DT" (0.000013)
2518. feature "THAP3" (0.000013)
2519. feature "FCHSD1" (0.000013)
2520. feature "HEIH" (0.000013)
2521. feature "AKR1E2" (0.000013)
2522. feature "ZIC4" (0.000013)
2523. feature "LINC00844" (0.000013)
2524. feature "POLR2M" (0.000013)
2525. feature "SLC45A4" (0.000013)
2526. feature "SEPTIN3" (0.000013)
2527. feature "S100A2" (0.000013)
2528. feature "FAXC" (0.000013)
2529. feature "ZNF573" (0.000013)
2530. feature "KNDC1" (0.000013)
2531. feature "DPY19L1" (0.000013)
2532. feature "MYO3B-AS1" (0.000013)
2533. feature "POMGNT2" (0.000013)
2534. feature "C8orf88" (0.000013)
2535. feature "SGSH" (0.000013)
2536. feature "CARM1" (0.000013)
2537. feature "EMP3" (0.000013)
2538. feature "RFNG" (0.000013)
2539. feature "ZNF391" (0.000013)
2540. feature "CLCN7" (0.000013)
2541. feature "TRMT61B" (0.000013)
2542. feature "PCDHGA2" (0.000013)
2543. feature "DNAJC25-GNG10" (0.000013)
2544. feature "ZNF324B" (0.000013)
2545. feature "TFPI2-DT" (0.000013)
2546. feature "KCTD2" (0.000013)
2547. feature "METTL7A" (0.000013)
2548. feature "ABCG1" (0.000012)
2549. feature "ZCCHC24" (0.000012)
2550. feature "ZNF112" (0.000012)
2551. feature "C19orf47" (0.000012)
2552. feature "PCDHGC3" (0.000012)
2553. feature "MAP3K12" (0.000012)
2554. feature "KCNE4" (0.000012)
2555. feature "SUCLA2-AS1" (0.000012)
2556. feature "MAP3K14" (0.000012)
2557. feature "CTU2" (0.000012)
2558. feature "RAD54L" (0.000012)
2559. feature "HAUS5" (0.000012)
2560. feature "SGMS1-AS1" (0.000012)
2561. feature "ABTB2" (0.000012)
2562. feature "JPH3" (0.000012)
2563. feature "SLC46A1" (0.000012)
2564. feature "TCL6" (0.000012)
2565. feature "CCNT2-AS1" (0.000012)
2566. feature "ALDH3A1" (0.000012)
2567. feature "LEF1" (0.000012)
2568. feature "ZBTB22" (0.000012)
2569. feature "FCRL5" (0.000012)
2570. feature "MAPK7" (0.000012)
2571. feature "SLC26A8" (0.000012)
2572. feature "TRAK1" (0.000012)
2573. feature "CCDC71L" (0.000012)
2574. feature "ARHGEF2" (0.000012)
2575. feature "PLEKHG1" (0.000012)
2576. feature "OR5M2P" (0.000012)
2577. feature "BMS1P22" (0.000012)
2578. feature "HMGN1P17" (0.000012)
2579. feature "TMEM104" (0.000012)
2580. feature "OSBPL5" (0.000012)
2581. feature "CNIH2" (0.000012)
2582. feature "IRF7" (0.000012)
2583. feature "SMPD4BP" (0.000012)
2584. feature "BICC1" (0.000012)
2585. feature "ABCC4" (0.000012)
2586. feature "OR10H1" (0.000012)
2587. feature "SARS2" (0.000012)
2588. feature "SLC24A2" (0.000012)
2589. feature "TPTEP2" (0.000012)
2590. feature "PCED1A" (0.000012)
2591. feature "SLC25A12" (0.000012)
2592. feature "CLCN6" (0.000012)
2593. feature "UNC5B" (0.000012)
2594. feature "CALHM2" (0.000012)
2595. feature "MT-TH" (0.000011)
2596. feature "ZNF862" (0.000011)
2597. feature "ULK3" (0.000011)
2598. feature "CMTM3" (0.000011)
2599. feature "FAM118B" (0.000011)
2600. feature "BRWD1-AS2" (0.000011)
2601. feature "SOX7" (0.000011)
2602. feature "RCOR2" (0.000011)
2603. feature "MIR3677HG" (0.000011)
2604. feature "RNF180" (0.000011)
2605. feature "TCF7" (0.000011)
2606. feature "RNU6-450P" (0.000011)
2607. feature "LINC02453" (0.000011)
2608. feature "LINC00598" (0.000011)
2609. feature "CLSTN3" (0.000011)
2610. feature "PCAT19" (0.000011)
2611. feature "ZNF597" (0.000011)
2612. feature "GLB1L" (0.000011)
2613. feature "EML6" (0.000011)
2614. feature "LINC01016" (0.000011)
2615. feature "MT-TL1" (0.000011)
2616. feature "ANKRD36BP2" (0.000011)
2617. feature "TUBG2" (0.000011)
2618. feature "LINC02605" (0.000011)
2619. feature "TMEM147-AS1" (0.000011)
2620. feature "DCLK1" (0.000011)
2621. feature "LINC00667" (0.000011)
2622. feature "SIMC1" (0.000011)
2623. feature "KIF9-AS1" (0.000011)
2624. feature "IGF2BP2" (0.000011)
2625. feature "KCNV1" (0.000011)
2626. feature "ATG16L2" (0.000011)
2627. feature "CAMK2B" (0.000011)
2628. feature "MPPED2" (0.000011)
2629. feature "ZNF446" (0.000011)
2630. feature "RBMS3" (0.000011)
2631. feature "IPO13" (0.000011)
2632. feature "CHMP6" (0.000011)
2633. feature "ZNF319" (0.000011)
2634. feature "SLC25A21-AS1" (0.000011)
2635. feature "ASH1L-AS1" (0.000011)
2636. feature "RPL23AP53" (0.000011)
2637. feature "NSRP1P1" (0.000011)
2638. feature "TAP1" (0.000011)
2639. feature "LRRC20" (0.000011)
2640. feature "FN3K" (0.000010)
2641. feature "CYP4B1" (0.000010)
2642. feature "OR2I1P" (0.000010)
2643. feature "KLRG2" (0.000010)
2644. feature "C12orf50" (0.000010)
2645. feature "C1orf112" (0.000010)
2646. feature "FOSL1" (0.000010)
2647. feature "PRRT2" (0.000010)
2648. feature "VPS37D" (0.000010)
2649. feature "UBE2L6" (0.000010)
2650. feature "VASN" (0.000010)
2651. feature "FJX1" (0.000010)
2652. feature "PANK4" (0.000010)
2653. feature "PRAG1" (0.000010)
2654. feature "LINC01293" (0.000010)
2655. feature "ZNF530" (0.000010)
2656. feature "VMAC" (0.000010)
2657. feature "MOGAT3" (0.000010)
2658. feature "RPL13P2" (0.000010)
2659. feature "TRIM66" (0.000010)
2660. feature "PINK1" (0.000010)
2661. feature "DNM3" (0.000010)
2662. feature "PACSIN1" (0.000010)
2663. feature "VDAC1P8" (0.000010)
2664. feature "MAPK8IP1" (0.000010)
2665. feature "OR7E122P" (0.000010)
2666. feature "TMCC3" (0.000010)
2667. feature "H2AC10P" (0.000010)
2668. feature "RPL12P25" (0.000010)
2669. feature "OGN" (0.000010)
2670. feature "VAX2" (0.000010)
2671. feature "NFKBIZ" (0.000010)
2672. feature "KATNAL2" (0.000010)
2673. feature "PPOX" (0.000010)
2674. feature "PDE4A" (0.000010)
2675. feature "ZNF772" (0.000010)
2676. feature "ADRB1" (0.000010)
2677. feature "DLX3" (0.000010)
2678. feature "DLG5-AS1" (0.000010)
2679. feature "NOMO3" (0.000010)
2680. feature "CTNS" (0.000010)
2681. feature "NDUFV2-AS1" (0.000010)
2682. feature "VPS13B-DT" (0.000010)
2683. feature "CORO1A" (0.000010)
2684. feature "SHISA2" (0.000010)
2685. feature "SLC25A42" (0.000010)
2686. feature "CEP72" (0.000010)
2687. feature "NES" (0.000010)
2688. feature "STRIT1" (0.000010)
2689. feature "LINC01907" (0.000009)
2690. feature "B3GNT6" (0.000009)
2691. feature "THNSL2" (0.000009)
2692. feature "TRPC3" (0.000009)
2693. feature "SNHG18" (0.000009)
2694. feature "TRNP1" (0.000009)
2695. feature "GNRHR2" (0.000009)
2696. feature "CDHR3" (0.000009)
2697. feature "PKN3" (0.000009)
2698. feature "HNF4A" (0.000009)
2699. feature "ZNF799" (0.000009)
2700. feature "GAS7" (0.000009)
2701. feature "HPSE" (0.000009)
2702. feature "CTSK" (0.000009)
2703. feature "SERPINB9" (0.000009)
2704. feature "SIPA1" (0.000009)
2705. feature "KIAA0753" (0.000009)
2706. feature "HARBI1" (0.000009)
2707. feature "MYO3B" (0.000009)
2708. feature "DPPA4" (0.000009)
2709. feature "CCT6B" (0.000009)
2710. feature "CDC42EP3" (0.000009)
2711. feature "TBXAS1" (0.000009)
2712. feature "SREK1IP1P1" (0.000009)
2713. feature "PKN2-AS1" (0.000009)
2714. feature "FBXO6" (0.000009)
2715. feature "CALR4P" (0.000009)
2716. feature "NDUFA6-DT" (0.000009)
2717. feature "CYP19A1" (0.000009)
2718. feature "CARF" (0.000009)
2719. feature "CCDC61" (0.000008)
2720. feature "RAMP3" (0.000008)
2721. feature "LENG8-AS1" (0.000008)
2722. feature "STX1A" (0.000008)
2723. feature "ANKMY1" (0.000008)
2724. feature "TUT4" (0.000008)
2725. feature "GCNA" (0.000008)
2726. feature "RPL34P1" (0.000008)
2727. feature "PDCD6IPP2" (0.000008)
2728. feature "CTHRC1" (0.000008)
2729. feature "POLM" (0.000008)
2730. feature "PCDHGA1" (0.000008)
2731. feature "ERO1B" (0.000008)
2732. feature "B3GNT7" (0.000008)
2733. feature "CPLX1" (0.000008)
2734. feature "SYT16" (0.000008)
2735. feature "RAPGEF5" (0.000008)
2736. feature "C1orf210" (0.000008)
2737. feature "NXPH4" (0.000008)
2738. feature "LINC01963" (0.000008)
2739. feature "ZNF81" (0.000008)
2740. feature "SLC25A22" (0.000008)
2741. feature "GYG2" (0.000008)
2742. feature "RNF207" (0.000008)
2743. feature "ELAC1" (0.000008)
2744. feature "PODNL1" (0.000008)
2745. feature "NLGN2" (0.000008)
2746. feature "DLG4" (0.000008)
2747. feature "TRIM69" (0.000008)
2748. feature "SLC22A23" (0.000008)
2749. feature "SERTAD2" (0.000008)
2750. feature "PAX5" (0.000008)
2751. feature "C3orf35" (0.000008)
2752. feature "LINC01427" (0.000008)
2753. feature "RFFL" (0.000008)
2754. feature "PLAGL1" (0.000008)
2755. feature "PRELID3A" (0.000007)
2756. feature "LINC00853" (0.000007)
2757. feature "CTIF" (0.000007)
2758. feature "OSGEPL1" (0.000007)
2759. feature "BEND3" (0.000007)
2760. feature "ESRRA" (0.000007)
2761. feature "XKR4" (0.000007)
2762. feature "BICRA" (0.000007)
2763. feature "RASAL1" (0.000007)
2764. feature "UCHL1" (0.000007)
2765. feature "KCTD17" (0.000007)
2766. feature "DNAAF8" (0.000007)
2767. feature "ZNF354C" (0.000007)
2768. feature "ZNF969P" (0.000007)
2769. feature "CATSPERB" (0.000007)
2770. feature "TMEM80" (0.000007)
2771. feature "SHOX2" (0.000007)
2772. feature "LINC01214" (0.000007)
2773. feature "OR9H1P" (0.000007)
2774. feature "ARRDC3-AS1" (0.000007)
2775. feature "SYCP3" (0.000007)
2776. feature "OXTR" (0.000007)
2777. feature "HMOX1" (0.000007)
2778. feature "RABL2A" (0.000007)
2779. feature "DENND6B" (0.000007)
2780. feature "USP7-AS1" (0.000007)
2781. feature "ENGASE" (0.000007)
2782. feature "MFSD14A" (0.000007)
2783. feature "SDCBP2-AS1" (0.000007)
2784. feature "PDZD2" (0.000007)
2785. feature "B3GNT5" (0.000007)
2786. feature "CRB3" (0.000007)
2787. feature "ZNF512" (0.000007)
2788. feature "ACTR5" (0.000007)
2789. feature "IKZF4" (0.000007)
2790. feature "ARMC2" (0.000007)
2791. feature "MMAA" (0.000007)
2792. feature "LZTR1" (0.000007)
2793. feature "CAPN10-DT" (0.000007)
2794. feature "ETV6" (0.000007)
2795. feature "PHF1" (0.000007)
2796. feature "RADIL" (0.000006)
2797. feature "ZNF25" (0.000006)
2798. feature "FGD3" (0.000006)
2799. feature "PCBP4" (0.000006)
2800. feature "CPEB2" (0.000006)
2801. feature "WDR90" (0.000006)
2802. feature "STAR" (0.000006)
2803. feature "TBC1D27P" (0.000006)
2804. feature "RNF19B" (0.000006)
2805. feature "PRTG" (0.000006)
2806. feature "KDM7A-DT" (0.000006)
2807. feature "ZBTB12" (0.000006)
2808. feature "ERVMER34-1" (0.000006)
2809. feature "CRAT" (0.000006)
2810. feature "KCNF1" (0.000006)
2811. feature "HOTAIR" (0.000006)
2812. feature "NIBAN3" (0.000006)
2813. feature "TAT-AS1" (0.000006)
2814. feature "RAP1GAP" (0.000006)
2815. feature "CDK18" (0.000006)
2816. feature "ITPRIP" (0.000006)
2817. feature "UBQLN1-AS1" (0.000006)
2818. feature "CAPN5" (0.000006)
2819. feature "HDAC4" (0.000006)
2820. feature "TRABD2A" (0.000006)
2821. feature "FAM184A" (0.000006)
2822. feature "COL6A1" (0.000005)
2823. feature "FANCE" (0.000005)
2824. feature "GIPR" (0.000005)
2825. feature "FAM86C2P" (0.000005)
2826. feature "SLC29A2" (0.000005)
2827. feature "TPPP3" (0.000005)
2828. feature "FUT2" (0.000005)
2829. feature "INAFM2" (0.000005)
2830. feature "ATP13A4" (0.000005)
2831. feature "CCDC102A" (0.000005)
2832. feature "ZNF615" (0.000005)
2833. feature "ACTG1P25" (0.000005)
2834. feature "FOXP3" (0.000005)
2835. feature "PLCG2" (0.000005)
2836. feature "GASK1B" (0.000005)
2837. feature "SCAMP5" (0.000005)
2838. feature "ZNF569" (0.000005)
2839. feature "PAQR3" (0.000005)
2840. feature "SHISA4" (0.000005)
2841. feature "LINC00352" (0.000005)
2842. feature "FHIP2B" (0.000005)
2843. feature "MT-TT" (0.000005)
2844. feature "EPHX2" (0.000005)
2845. feature "RPS6P26" (0.000005)
2846. feature "PCDH11X" (0.000005)
2847. feature "TMCO6" (0.000005)
2848. feature "PLEKHS1" (0.000005)
2849. feature "MFAP3" (0.000005)
2850. feature "PSEN2" (0.000005)
2851. feature "SIRT3" (0.000005)
2852. feature "MTCO1P40" (0.000005)
2853. feature "TMCC1-DT" (0.000005)
2854. feature "EPS15-AS1" (0.000005)
2855. feature "GCNT2" (0.000005)
2856. feature "ARHGEF40" (0.000005)
2857. feature "DUSP7" (0.000005)
2858. feature "DPY19L2P1" (0.000005)
2859. feature "RGMB" (0.000005)
2860. feature "FGFR2" (0.000005)
2861. feature "LINC02163" (0.000005)
2862. feature "RPL10AP6" (0.000005)
2863. feature "SLC4A11" (0.000005)
2864. feature "ELAPOR2" (0.000005)
2865. feature "SMIM3" (0.000005)
2866. feature "CHST6" (0.000004)
2867. feature "MAFA" (0.000004)
2868. feature "LINC00680" (0.000004)
2869. feature "SYNM" (0.000004)
2870. feature "ZDHHC8" (0.000004)
2871. feature "STOX1" (0.000004)
2872. feature "LINC00885" (0.000004)
2873. feature "TTPA" (0.000004)
2874. feature "P2RX7" (0.000004)
2875. feature "RNF157" (0.000004)
2876. feature "LIMD2" (0.000004)
2877. feature "WDR19" (0.000004)
2878. feature "LINC01355" (0.000004)
2879. feature "LINC00513" (0.000004)
2880. feature "GHDC" (0.000004)
2881. feature "LINC02080" (0.000004)
2882. feature "GPR68" (0.000004)
2883. feature "LPP-AS2" (0.000004)
2884. feature "ABHD8" (0.000004)
2885. feature "OTUB2" (0.000004)
2886. feature "SCMH1" (0.000004)
2887. feature "LINC01270" (0.000004)
2888. feature "NAP1L6P" (0.000004)
2889. feature "LINC02710" (0.000004)
2890. feature "HROB" (0.000004)
2891. feature "GCM1" (0.000004)
2892. feature "P2RY2" (0.000004)
2893. feature "TIRAP" (0.000004)
2894. feature "ASAH2B" (0.000004)
2895. feature "ZNF90" (0.000004)
2896. feature "SMOX" (0.000004)
2897. feature "NDRG4" (0.000004)
2898. feature "IL18BP" (0.000004)
2899. feature "NKAIN1" (0.000003)
2900. feature "H2AZ2P1" (0.000003)
2901. feature "FAM214B" (0.000003)
2902. feature "NEBL-AS1" (0.000003)
2903. feature "MT-TK" (0.000003)
2904. feature "IPO8P1" (0.000003)
2905. feature "RAB4B" (0.000003)
2906. feature "FER1L5" (0.000003)
2907. feature "EFNB1" (0.000003)
2908. feature "TMEM191C" (0.000003)
2909. feature "KCNS3" (0.000003)
2910. feature "ARHGEF26-AS1" (0.000003)
2911. feature "FRAS1" (0.000003)
2912. feature "PARS2" (0.000003)
2913. feature "HSH2D" (0.000003)
2914. feature "NPHP4" (0.000003)
2915. feature "GUCY1B2" (0.000003)
2916. feature "VEPH1" (0.000003)
2917. feature "F3" (0.000003)
2918. feature "MARCKSL1P2" (0.000003)
2919. feature "TMEM216" (0.000003)
2920. feature "LINC01722" (0.000003)
2921. feature "CAB39L" (0.000003)
2922. feature "AIMP1P1" (0.000003)
2923. feature "NAT8L" (0.000003)
2924. feature "C5orf64" (0.000003)
2925. feature "TMEM169" (0.000003)
2926. feature "ATG4C" (0.000003)
2927. feature "GALNT18" (0.000003)
2928. feature "CA4" (0.000003)
2929. feature "OAF" (0.000002)
2930. feature "SUNO1" (0.000002)
2931. feature "BBS7-DT" (0.000002)
2932. feature "KLHL21" (0.000002)
2933. feature "ZNF286B" (0.000002)
2934. feature "KHDC1" (0.000002)
2935. feature "GUCY1A2" (0.000002)
2936. feature "NARF-AS2" (0.000002)
2937. feature "PSMC1P10" (0.000002)
2938. feature "PGAP3" (0.000002)
2939. feature "PSORS1C1" (0.000002)
2940. feature "RBM12B-AS1" (0.000002)
2941. feature "STAMBPL1" (0.000002)
2942. feature "RUSC2" (0.000002)
2943. feature "DIRC3" (0.000002)
2944. feature "SAMD10" (0.000002)
2945. feature "TRIM31" (0.000002)
2946. feature "MC2R" (0.000002)
2947. feature "AARSD1" (0.000002)
2948. feature "IL17D" (0.000002)
2949. feature "DRC3" (0.000002)
2950. feature "LINC00997" (0.000002)
2951. feature "TSHR" (0.000002)
2952. feature "TNS4" (0.000002)
2953. feature "LENG9" (0.000002)
2954. feature "KCNG1" (0.000002)
2955. feature "LY6E-DT" (0.000002)
2956. feature "WASH6P" (0.000002)
2957. feature "IFFO2" (0.000002)
2958. feature "ZNF865" (0.000002)
2959. feature "DISC1" (0.000002)
2960. feature "NPTXR" (0.000002)
2961. feature "LINC01637" (0.000002)
2962. feature "ZNF436" (0.000002)
2963. feature "BMP8A" (0.000001)
2964. feature "EID2B" (0.000001)
2965. feature "MAS1" (0.000001)
2966. feature "GPR153" (0.000001)
2967. feature "SH3BP5L" (0.000001)
2968. feature "RFLNB" (0.000000)
2969. feature "MUC5AC" (0.000000)
2970. feature "FSTL4" (0.000000)
2971. feature "RUBCN" (0.000000)
2972. feature "WDR81" (0.000000)
2973. feature "SYNPO2L" (0.000000)
2974. feature "DMTN" (0.000000)
2975. feature "GOLGA8B" (0.000000)
2976. feature "TCF4" (0.000000)
2977. feature "PDZRN3" (0.000000)
2978. feature "ZNF674-AS1" (0.000000)
2979. feature "MTRNR2L4" (0.000000)
2980. feature "LINC00271" (0.000000)
2981. feature "STAG3" (0.000000)
2982. feature "UQCRHL" (0.000000)
2983. feature "LRRC8E" (0.000000)
2984. feature "GNAZ" (0.000000)
2985. feature "COLCA1" (0.000000)
2986. feature "ARL6" (0.000000)
2987. feature "TRIM62" (0.000000)
2988. feature "NCF2" (0.000000)
2989. feature "C9orf152" (0.000000)
2990. feature "ZNF30" (0.000000)
2991. feature "SCRN2" (0.000000)
2992. feature "TBCCD1" (0.000000)
2993. feature "SNN" (0.000000)
2994. feature "ACTA2" (0.000000)
2995. feature "PDE10A" (0.000000)
2996. feature "OR4F13P" (0.000000)
2997. feature "PPT2" (0.000000)
2998. feature "ZBTB49" (0.000000)
2999. feature "LINC00863" (0.000000)
3000. feature "ZNF571-AS1" (0.000000)

¶

4.3.3) XGBoost and Adaptive Boosting Classifier

Also in this case we tried to see if the Xgboost predictor could beat the performance of the Random forest classifier.

Indeed it happened, we built the model as the previous one, but augmenting the parameter search with the same numbers of the random forest classifier.

We were satisfied as the performance of this model was slightly better, indeed it achieves a precision of 97,70%.

Also in this case the gene MT_RNR2 was very high in the ranking of the predictors.

In [ ]:
#i build a XGBoost classifier for the DropSeq MCF7 dataset to see if it performs better than the random forest classifier

# Define the XGBoost classifier
xgb = XGBClassifier(random_state=42)

# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [50, 60, 100], 'max_depth': [30, 50, 60]}
grid_search = GridSearchCV(xgb, param_grid, cv=5)

outer_cv = KFold(n_splits=5, shuffle=True, random_state=42)

# Perform the cross validation
scores = cross_val_score(grid_search, X_train_df_DS_MCF7, y_train_df_DS_MCF7, cv=outer_cv)

# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))

# Perform GridSearchCV on the entire dataset to get the best parameters and fit the model
grid_search.fit(X_train_df_DS_MCF7, y_train_df_DS_MCF7)
print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
print("Best model: ", grid_search.best_estimator_)
print("Test accuracy: ", grid_search.best_estimator_.score(X_test_df_DS_MCF7, y_test_df_DS_MCF7))

# Print the feature importances
importances = grid_search.best_estimator_.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_df_DS_MCF7.shape[1]):
    print("%d. feature %s (%f)" % (f + 1, X_train_df_DS_MCF7.columns[indices[f]], importances[indices[f]]))

best_xgb_DS_MCF7 = grid_search.best_estimator_

Mean accuracy of nested cross validation:  0.9766473988439307
Best hyperparameters:  {'max_depth': 50, 'n_estimators': 100}
Best accuracy:  0.976878612716763
Best model:  XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=50, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=100, n_jobs=None,
              num_parallel_tree=None, random_state=42, ...)
Test accuracy:  0.9798890429958391
Feature ranking:
1. feature "TFF1" (0.046183)
2. feature "GAPDH" (0.045575)
3. feature "MT-RNR2" (0.033950)
4. feature "KRT19" (0.027494)
5. feature "PGK1" (0.026391)
6. feature "LGALS1" (0.022712)
7. feature "TMSB10" (0.018845)
8. feature "MT-RNR1" (0.017575)
9. feature "RNPS1" (0.008641)
10. feature "MT-ND3" (0.006656)
11. feature "MT-ND4" (0.006061)
12. feature "MT-CO3" (0.005830)
13. feature "ENO1" (0.005807)
14. feature "MT-ND1" (0.005790)
15. feature "MT-CYB" (0.005780)
16. feature "TPI1" (0.005340)
17. feature "MT-ND2" (0.005092)
18. feature "LIMK1" (0.005059)
19. feature "RPS19" (0.004945)
20. feature "NDUFAF8" (0.004617)
21. feature "ZNF326" (0.004367)
22. feature "ZMYND11" (0.004317)
23. feature "RPS14" (0.004219)
24. feature "PPP4R3A" (0.004003)
25. feature "MT-TS1" (0.003910)
26. feature "PKM" (0.003838)
27. feature "HMGB2" (0.003778)
28. feature "DNAJA3" (0.003773)
29. feature "METRN" (0.003753)
30. feature "ROCK1" (0.003692)
31. feature "SYTL2" (0.003690)
32. feature "KCNQ1OT1" (0.003664)
33. feature "RIPK2" (0.003655)
34. feature "MT-ATP6" (0.003639)
35. feature "RPL13" (0.003486)
36. feature "PRR12" (0.003460)
37. feature "LRRCC1" (0.003242)
38. feature "IFI27L2" (0.003217)
39. feature "AAMP" (0.003180)
40. feature "RAD23B" (0.003125)
41. feature "SPACA6" (0.003035)
42. feature "RPS27L" (0.003005)
43. feature "WSB2" (0.002999)
44. feature "SETD2" (0.002954)
45. feature "CSK" (0.002896)
46. feature "PPP2R3B" (0.002865)
47. feature "NRBP1" (0.002862)
48. feature "FARSA" (0.002860)
49. feature "MT-ND6" (0.002853)
50. feature "DDX28" (0.002852)
51. feature "ZNF121" (0.002848)
52. feature "SAPCD2" (0.002847)
53. feature "TIMELESS" (0.002769)
54. feature "SYCP2" (0.002730)
55. feature "MIOS" (0.002707)
56. feature "MT-CO2" (0.002704)
57. feature "PPFIA1" (0.002695)
58. feature "SOGA1" (0.002642)
59. feature "CDC42SE1" (0.002641)
60. feature "CNOT2" (0.002630)
61. feature "EIF3J" (0.002625)
62. feature "HES1" (0.002592)
63. feature "RPLP0P2" (0.002552)
64. feature "PTGES2" (0.002520)
65. feature "EIF2B4" (0.002512)
66. feature "INCENP" (0.002506)
67. feature "TMEM64" (0.002476)
68. feature "FASN" (0.002460)
69. feature "DSP" (0.002449)
70. feature "GFOD1" (0.002441)
71. feature "TOB1" (0.002436)
72. feature "CDK2AP1" (0.002433)
73. feature "MAFF" (0.002432)
74. feature "RAP1A" (0.002404)
75. feature "ZHX1" (0.002342)
76. feature "ABCB9" (0.002337)
77. feature "DTX3" (0.002334)
78. feature "ZFC3H1" (0.002328)
79. feature "KPNA1" (0.002315)
80. feature "SMIM27" (0.002297)
81. feature "STUB1" (0.002288)
82. feature "MT-ND4L" (0.002277)
83. feature "NSRP1" (0.002275)
84. feature "PTBP3" (0.002271)
85. feature "RPL15" (0.002220)
86. feature "ZZZ3" (0.002210)
87. feature "RPL35" (0.002173)
88. feature "SETD3" (0.002152)
89. feature "RNF32" (0.002124)
90. feature "C14orf28" (0.002115)
91. feature "ARHGEF12" (0.002096)
92. feature "SCRIB" (0.002089)
93. feature "OR7E25P" (0.002064)
94. feature "MIR663AHG" (0.002056)
95. feature "LARP4B" (0.002054)
96. feature "MGP" (0.002054)
97. feature "RFNG" (0.002027)
98. feature "MRGBP" (0.002008)
99. feature "MYO10" (0.001994)
100. feature "ATMIN" (0.001974)
101. feature "GSPT1" (0.001960)
102. feature "SOX4" (0.001956)
103. feature "C8orf82" (0.001952)
104. feature "C5" (0.001949)
105. feature "MT-ND5" (0.001931)
106. feature "ARHGAP1" (0.001925)
107. feature "TFF3" (0.001917)
108. feature "DENND3" (0.001906)
109. feature "ATP5MK" (0.001899)
110. feature "UBALD1" (0.001892)
111. feature "TMEM115" (0.001879)
112. feature "NMI" (0.001855)
113. feature "MT-CO1" (0.001845)
114. feature "SLC25A24" (0.001841)
115. feature "RPL34" (0.001839)
116. feature "PLCD3" (0.001834)
117. feature "KIAA1522" (0.001817)
118. feature "BAP1" (0.001803)
119. feature "SOCS2" (0.001793)
120. feature "VPS16" (0.001788)
121. feature "ATRX" (0.001787)
122. feature "GOLGA5" (0.001787)
123. feature "H4C3" (0.001781)
124. feature "MARK2" (0.001774)
125. feature "RPL12" (0.001769)
126. feature "RPL30" (0.001766)
127. feature "RPS5" (0.001760)
128. feature "PLEKHF2" (0.001747)
129. feature "DPH1" (0.001746)
130. feature "MED13" (0.001745)
131. feature "HSPH1" (0.001727)
132. feature "TAF13" (0.001724)
133. feature "AXL" (0.001722)
134. feature "UGCG" (0.001721)
135. feature "NEAT1" (0.001719)
136. feature "MRPL20-AS1" (0.001717)
137. feature "EHBP1" (0.001707)
138. feature "ZFP36L1" (0.001702)
139. feature "RPL39" (0.001701)
140. feature "MT-TQ" (0.001698)
141. feature "S100A10" (0.001689)
142. feature "ZNF26" (0.001686)
143. feature "GABPB2" (0.001683)
144. feature "KLHL7" (0.001680)
145. feature "NIPBL" (0.001679)
146. feature "MYBL1" (0.001656)
147. feature "DYNLL2" (0.001652)
148. feature "KITLG" (0.001621)
149. feature "TPM3" (0.001611)
150. feature "GPM6A" (0.001611)
151. feature "SNRPD2" (0.001603)
152. feature "MBD5" (0.001601)
153. feature "BCYRN1" (0.001599)
154. feature "UBA52" (0.001593)
155. feature "H1-5" (0.001591)
156. feature "CHERP" (0.001585)
157. feature "ZMYM3" (0.001578)
158. feature "UQCRQ" (0.001571)
159. feature "LMX1B" (0.001569)
160. feature "RTF1" (0.001564)
161. feature "FGF23" (0.001557)
162. feature "HAP1" (0.001551)
163. feature "SET" (0.001550)
164. feature "HNRNPUL1" (0.001549)
165. feature "ITPRID2" (0.001539)
166. feature "KRT80" (0.001526)
167. feature "PITPNA" (0.001525)
168. feature "SYNE2" (0.001513)
169. feature "MUC5B" (0.001505)
170. feature "OTULIN" (0.001492)
171. feature "POLR2J" (0.001483)
172. feature "DCLRE1B" (0.001480)
173. feature "B3GALT9" (0.001472)
174. feature "MALAT1" (0.001463)
175. feature "UHRF1BP1" (0.001453)
176. feature "PARD6B" (0.001446)
177. feature "NPLOC4" (0.001445)
178. feature "ZNF316" (0.001439)
179. feature "C6orf62" (0.001425)
180. feature "EHD1" (0.001424)
181. feature "MT-TV" (0.001422)
182. feature "SUDS3" (0.001401)
183. feature "PAPOLA" (0.001393)
184. feature "GATAD2A" (0.001393)
185. feature "PREX1" (0.001393)
186. feature "NDUFB2" (0.001391)
187. feature "DNMT1" (0.001391)
188. feature "RRP1B" (0.001388)
189. feature "H2AC12" (0.001387)
190. feature "TASOR2" (0.001378)
191. feature "PRPF40A" (0.001362)
192. feature "PIGS" (0.001357)
193. feature "ZC3H18" (0.001353)
194. feature "EPB41L1" (0.001345)
195. feature "PLBD2" (0.001330)
196. feature "C7orf50" (0.001327)
197. feature "ROMO1" (0.001327)
198. feature "HIF1A" (0.001319)
199. feature "KMT2B" (0.001316)
200. feature "HDGF" (0.001316)
201. feature "HNRNPA2B1" (0.001315)
202. feature "KRT18" (0.001311)
203. feature "MAPK1IP1L" (0.001310)
204. feature "IMPA2" (0.001304)
205. feature "TPX2" (0.001300)
206. feature "PDAP1" (0.001298)
207. feature "RPS15" (0.001298)
208. feature "H2AC20" (0.001295)
209. feature "FBXO27" (0.001292)
210. feature "MAN1A2" (0.001288)
211. feature "H2AC11" (0.001288)
212. feature "SPAG9" (0.001277)
213. feature "ZBTB20" (0.001274)
214. feature "EBAG9" (0.001267)
215. feature "C9orf78" (0.001264)
216. feature "CEP55" (0.001254)
217. feature "RPL37A" (0.001246)
218. feature "CBX3" (0.001234)
219. feature "NFX1" (0.001232)
220. feature "RPLP2" (0.001225)
221. feature "MCL1" (0.001224)
222. feature "PSMC5" (0.001220)
223. feature "ARF3" (0.001220)
224. feature "UBE2K" (0.001214)
225. feature "TSHZ1" (0.001213)
226. feature "VMP1" (0.001202)
227. feature "TRMT12" (0.001200)
228. feature "LARP1" (0.001194)
229. feature "TNRC6A" (0.001192)
230. feature "RPL29" (0.001189)
231. feature "ANKIB1" (0.001188)
232. feature "IRAK1" (0.001181)
233. feature "RPS16" (0.001176)
234. feature "UGDH" (0.001172)
235. feature "H1-4" (0.001171)
236. feature "MYH9" (0.001169)
237. feature "ZNF444" (0.001168)
238. feature "SPIN1" (0.001164)
239. feature "MAVS" (0.001164)
240. feature "PACS1" (0.001161)
241. feature "PSME4" (0.001156)
242. feature "MT-ATP8" (0.001150)
243. feature "RPL36" (0.001147)
244. feature "DDX21" (0.001147)
245. feature "RPL23" (0.001146)
246. feature "POLDIP2" (0.001145)
247. feature "SLC9A3R1" (0.001140)
248. feature "UQCC2" (0.001137)
249. feature "SRGAP1" (0.001137)
250. feature "POLD2" (0.001136)
251. feature "FAM13B" (0.001131)
252. feature "JAK1" (0.001127)
253. feature "RILPL2" (0.001124)
254. feature "TRIM37" (0.001122)
255. feature "CACNG4" (0.001119)
256. feature "BOD1L1" (0.001116)
257. feature "BCAS3" (0.001109)
258. feature "RPL12P38" (0.001109)
259. feature "CHN1" (0.001108)
260. feature "PHF8" (0.001107)
261. feature "UBE2Q2" (0.001104)
262. feature "KCTD15" (0.001102)
263. feature "TPM4" (0.001102)
264. feature "DROSHA" (0.001099)
265. feature "CAV1" (0.001096)
266. feature "REST" (0.001092)
267. feature "ARPP19" (0.001085)
268. feature "SSRP1" (0.001080)
269. feature "LEO1" (0.001073)
270. feature "DDX54" (0.001072)
271. feature "CACNA1A" (0.001068)
272. feature "STRBP" (0.001066)
273. feature "FAM177A1" (0.001063)
274. feature "COX7A2" (0.001059)
275. feature "HNRNPD" (0.001058)
276. feature "KIF21A" (0.001057)
277. feature "TMEM258" (0.001055)
278. feature "AP1G1" (0.001054)
279. feature "BRIP1" (0.001052)
280. feature "MAPKAPK2" (0.001046)
281. feature "RSRC2" (0.001044)
282. feature "C8orf33" (0.001031)
283. feature "NAA50" (0.001029)
284. feature "CETN3" (0.001023)
285. feature "RPS27" (0.001021)
286. feature "SLITRK5" (0.001020)
287. feature "ALKBH5" (0.001010)
288. feature "RPS15A" (0.001006)
289. feature "SERF2" (0.001001)
290. feature "GOLGA3" (0.001000)
291. feature "CDC42BPB" (0.000993)
292. feature "LINC01304" (0.000992)
293. feature "GPATCH4" (0.000991)
294. feature "STARD10" (0.000990)
295. feature "RRP15" (0.000988)
296. feature "SRSF1" (0.000982)
297. feature "EEA1" (0.000981)
298. feature "NDUFS6" (0.000971)
299. feature "RPL28" (0.000970)
300. feature "TRAK1" (0.000969)
301. feature "SECISBP2L" (0.000967)
302. feature "PRKACA" (0.000955)
303. feature "AP2S1" (0.000954)
304. feature "USP38" (0.000952)
305. feature "PAK2" (0.000950)
306. feature "GSE1" (0.000947)
307. feature "ANKRD11" (0.000943)
308. feature "UQCC3" (0.000942)
309. feature "PRRC2C" (0.000941)
310. feature "ROCK2" (0.000939)
311. feature "LMNB2" (0.000930)
312. feature "ARFGEF1" (0.000929)
313. feature "ZNRF1" (0.000929)
314. feature "H1-0" (0.000923)
315. feature "CENPF" (0.000922)
316. feature "DTYMK" (0.000916)
317. feature "LYAR" (0.000915)
318. feature "ELOB" (0.000914)
319. feature "RPS6KB1" (0.000911)
320. feature "EIF3A" (0.000910)
321. feature "PFDN4" (0.000909)
322. feature "ZNF282" (0.000908)
323. feature "PRRG3" (0.000902)
324. feature "S100A11" (0.000901)
325. feature "PTP4A2" (0.000899)
326. feature "TTLL12" (0.000891)
327. feature "KCNJ2" (0.000889)
328. feature "PKIB" (0.000889)
329. feature "REV1" (0.000887)
330. feature "SPATS2L" (0.000887)
331. feature "NET1" (0.000883)
332. feature "MAGEF1" (0.000882)
333. feature "IGFBP5" (0.000877)
334. feature "RPLP1" (0.000875)
335. feature "PSMG3-AS1" (0.000871)
336. feature "YWHAZ" (0.000868)
337. feature "RPL11" (0.000866)
338. feature "RPS12" (0.000864)
339. feature "TPD52L1" (0.000861)
340. feature "SRM" (0.000859)
341. feature "HNRNPH3" (0.000859)
342. feature "RPL37" (0.000858)
343. feature "RPL35A" (0.000850)
344. feature "USP8" (0.000846)
345. feature "ARID4B" (0.000842)
346. feature "SMIM10L1" (0.000832)
347. feature "CAPZA1" (0.000827)
348. feature "PABPN1" (0.000825)
349. feature "BCAR1" (0.000822)
350. feature "MIPOL1" (0.000819)
351. feature "YWHAB" (0.000817)
352. feature "LCA5L" (0.000804)
353. feature "CPEB4" (0.000793)
354. feature "CNBP" (0.000780)
355. feature "BCL3" (0.000779)
356. feature "ACTB" (0.000778)
357. feature "H19" (0.000775)
358. feature "MGRN1" (0.000775)
359. feature "HEPACAM" (0.000773)
360. feature "MKI67" (0.000771)
361. feature "SPDL1" (0.000770)
362. feature "ZNF217" (0.000769)
363. feature "YES1" (0.000765)
364. feature "ATP5ME" (0.000765)
365. feature "NCL" (0.000765)
366. feature "HELB" (0.000764)
367. feature "ARL13B" (0.000763)
368. feature "ANP32E" (0.000761)
369. feature "WDR43" (0.000758)
370. feature "PITX1" (0.000752)
371. feature "CLTB" (0.000751)
372. feature "H1-2" (0.000747)
373. feature "SUPT5H" (0.000738)
374. feature "S100P" (0.000738)
375. feature "CEP78" (0.000737)
376. feature "NPM1P40" (0.000733)
377. feature "PMEPA1" (0.000731)
378. feature "FTL" (0.000730)
379. feature "DNAJA1" (0.000730)
380. feature "CDIPT" (0.000728)
381. feature "RHOBTB3" (0.000727)
382. feature "EFNA5" (0.000726)
383. feature "VPS18" (0.000721)
384. feature "KIF5B" (0.000721)
385. feature "UBE2Q1" (0.000718)
386. feature "RPS28" (0.000717)
387. feature "ZBTB37" (0.000714)
388. feature "PCGF3" (0.000701)
389. feature "BASP1" (0.000699)
390. feature "GOLGA4" (0.000699)
391. feature "EPN3" (0.000693)
392. feature "SPRYD3" (0.000688)
393. feature "FAM102A" (0.000686)
394. feature "RNF122" (0.000681)
395. feature "SUNO1" (0.000677)
396. feature "GYS1" (0.000677)
397. feature "BPTF" (0.000677)
398. feature "CDAN1" (0.000674)
399. feature "NUP62" (0.000673)
400. feature "SMC6" (0.000672)
401. feature "FOXJ3" (0.000669)
402. feature "OAZ3" (0.000667)
403. feature "SLAIN2" (0.000665)
404. feature "EFCAB5" (0.000664)
405. feature "ZDHHC18" (0.000663)
406. feature "EIF4G2" (0.000662)
407. feature "CENPE" (0.000661)
408. feature "DGKZ" (0.000659)
409. feature "DSCAM-AS1" (0.000657)
410. feature "KRT8" (0.000656)
411. feature "EPAS1" (0.000653)
412. feature "MRPL33" (0.000649)
413. feature "CSDE1" (0.000643)
414. feature "FOXK1" (0.000642)
415. feature "SACS" (0.000641)
416. feature "SOCS4" (0.000641)
417. feature "GNAQ" (0.000640)
418. feature "CHAF1A" (0.000639)
419. feature "GATA3" (0.000639)
420. feature "SEPTIN8" (0.000636)
421. feature "PCGF2" (0.000636)
422. feature "BAZ1B" (0.000631)
423. feature "DCTN5" (0.000627)
424. feature "GARS1" (0.000626)
425. feature "GALNT1" (0.000626)
426. feature "H1-3" (0.000624)
427. feature "SMARCD2" (0.000622)
428. feature "HNRNPAB" (0.000620)
429. feature "PEF1" (0.000618)
430. feature "MDM4" (0.000617)
431. feature "SUSD6" (0.000616)
432. feature "WWP1" (0.000611)
433. feature "LINC00205" (0.000609)
434. feature "JMJD8" (0.000607)
435. feature "RAB13" (0.000606)
436. feature "KRT10" (0.000603)
437. feature "NAA16" (0.000595)
438. feature "GRK6" (0.000594)
439. feature "NCBP3" (0.000593)
440. feature "MAP3K9" (0.000591)
441. feature "GNL3L" (0.000591)
442. feature "SEPTIN11" (0.000590)
443. feature "FMNL3" (0.000586)
444. feature "NEDD8" (0.000584)
445. feature "FAM83H" (0.000583)
446. feature "DKC1" (0.000582)
447. feature "ZBTB42" (0.000580)
448. feature "ESRP2" (0.000580)
449. feature "ZNF578" (0.000579)
450. feature "NKIRAS2" (0.000579)
451. feature "ZNF510" (0.000578)
452. feature "USP1" (0.000577)
453. feature "PPM1G" (0.000573)
454. feature "CKS2" (0.000572)
455. feature "TEPSIN" (0.000570)
456. feature "GTF3A" (0.000570)
457. feature "ZC3H15" (0.000568)
458. feature "ZNF232" (0.000566)
459. feature "HSPB1" (0.000563)
460. feature "EIF4G3" (0.000560)
461. feature "NSD1" (0.000554)
462. feature "ITPK1" (0.000544)
463. feature "GBP1P1" (0.000543)
464. feature "NORAD" (0.000541)
465. feature "COX7C" (0.000538)
466. feature "INPP4B" (0.000538)
467. feature "PCSK5" (0.000536)
468. feature "PARM1" (0.000534)
469. feature "FZD1" (0.000534)
470. feature "ANKRD17" (0.000529)
471. feature "PLEC" (0.000526)
472. feature "CASP8AP2" (0.000525)
473. feature "PLA2G15" (0.000520)
474. feature "SAMD12" (0.000517)
475. feature "R3HDM1" (0.000514)
476. feature "FAM214A" (0.000510)
477. feature "SENP6" (0.000507)
478. feature "NCOA3" (0.000500)
479. feature "NCALD" (0.000496)
480. feature "PSMA7" (0.000496)
481. feature "HNRNPU" (0.000495)
482. feature "G6PD" (0.000494)
483. feature "CAMSAP2" (0.000491)
484. feature "MDM2" (0.000491)
485. feature "PABPC1" (0.000491)
486. feature "SRC" (0.000491)
487. feature "KCTD18" (0.000485)
488. feature "TRIP12" (0.000483)
489. feature "DVL3" (0.000481)
490. feature "DFFB" (0.000474)
491. feature "SRP14-DT" (0.000473)
492. feature "RAD21" (0.000470)
493. feature "MAFK" (0.000469)
494. feature "ANP32B" (0.000468)
495. feature "NAA10" (0.000468)
496. feature "CMSS1" (0.000464)
497. feature "SMC4" (0.000463)
498. feature "FGF12" (0.000461)
499. feature "MLX" (0.000460)
500. feature "L3HYPDH" (0.000460)
501. feature "CTHRC1" (0.000459)
502. feature "MT2A" (0.000459)
503. feature "BOLA3" (0.000458)
504. feature "SVIP" (0.000457)
505. feature "MAP3K14-AS1" (0.000457)
506. feature "CACNB2" (0.000454)
507. feature "XBP1" (0.000454)
508. feature "TPM1" (0.000453)
509. feature "OR2AT4" (0.000452)
510. feature "GADD45GIP1" (0.000452)
511. feature "HCFC1" (0.000452)
512. feature "HTATSF1" (0.000452)
513. feature "VAV2" (0.000448)
514. feature "BRD7" (0.000443)
515. feature "SLK" (0.000441)
516. feature "CENPB" (0.000438)
517. feature "EFCAB14" (0.000438)
518. feature "NBN" (0.000437)
519. feature "RSPH1" (0.000436)
520. feature "HMOX1" (0.000434)
521. feature "ZFP82" (0.000433)
522. feature "COTL1" (0.000432)
523. feature "SLC25A25-AS1" (0.000427)
524. feature "ILF3" (0.000424)
525. feature "TARS1" (0.000421)
526. feature "ATAD2" (0.000420)
527. feature "RBBP6" (0.000420)
528. feature "EML6" (0.000417)
529. feature "CALM2" (0.000416)
530. feature "SLC13A5" (0.000414)
531. feature "HSPA4" (0.000414)
532. feature "PRXL2C" (0.000414)
533. feature "TSPYL1" (0.000411)
534. feature "MTMR12" (0.000410)
535. feature "EIF5B" (0.000409)
536. feature "ATP1A1" (0.000408)
537. feature "SMC5" (0.000408)
538. feature "ZNF777" (0.000407)
539. feature "CDC42EP1" (0.000403)
540. feature "ZNF696" (0.000400)
541. feature "NHS" (0.000400)
542. feature "CLIP1" (0.000397)
543. feature "DDX19A" (0.000396)
544. feature "BRD4" (0.000396)
545. feature "PAPOLA-DT" (0.000392)
546. feature "ABL1" (0.000389)
547. feature "PPP2R5D" (0.000386)
548. feature "HSPD1" (0.000386)
549. feature "NEDD4L" (0.000385)
550. feature "ENAH" (0.000385)
551. feature "USP32" (0.000385)
552. feature "ZNF165" (0.000385)
553. feature "PHF3" (0.000379)
554. feature "AFF1" (0.000379)
555. feature "CAPN13" (0.000378)
556. feature "EIF4EBP2" (0.000377)
557. feature "FRS2" (0.000373)
558. feature "TOP2A" (0.000371)
559. feature "RAB17" (0.000368)
560. feature "SLTM" (0.000368)
561. feature "ACKR3" (0.000368)
562. feature "NF2" (0.000366)
563. feature "ZNF85" (0.000365)
564. feature "KPNA2" (0.000365)
565. feature "NCOR2" (0.000364)
566. feature "CDV3" (0.000356)
567. feature "POLK" (0.000354)
568. feature "TRIB3" (0.000353)
569. feature "NPFFR1" (0.000350)
570. feature "S1PR2" (0.000343)
571. feature "RPS12P16" (0.000342)
572. feature "KIF18B" (0.000341)
573. feature "FBP1" (0.000340)
574. feature "PGAM5" (0.000339)
575. feature "LRCH3" (0.000336)
576. feature "FGD5-AS1" (0.000336)
577. feature "HELZ2" (0.000332)
578. feature "GPBP1" (0.000332)
579. feature "PHACTR2" (0.000327)
580. feature "NRIP1" (0.000325)
581. feature "ZNF731P" (0.000324)
582. feature "NSMAF" (0.000321)
583. feature "PPP1R37" (0.000320)
584. feature "CAST" (0.000320)
585. feature "MAP7D1" (0.000319)
586. feature "ECHDC2" (0.000317)
587. feature "ELOA" (0.000312)
588. feature "DHTKD1" (0.000310)
589. feature "DCTN6-DT" (0.000309)
590. feature "SNHG30" (0.000307)
591. feature "CMPK1" (0.000304)
592. feature "PTPN11" (0.000302)
593. feature "CTXN1" (0.000299)
594. feature "MPHOSPH9" (0.000297)
595. feature "OR2I1P" (0.000297)
596. feature "ONECUT2" (0.000296)
597. feature "SENP3" (0.000293)
598. feature "MRPS34" (0.000291)
599. feature "KLHL20" (0.000290)
600. feature "HSPD1P11" (0.000290)
601. feature "BAZ2A" (0.000290)
602. feature "TIAL1" (0.000288)
603. feature "TNFAIP8L3" (0.000287)
604. feature "METAP2" (0.000287)
605. feature "LRRC69" (0.000286)
606. feature "DEK" (0.000285)
607. feature "RNF34" (0.000283)
608. feature "MGLL" (0.000283)
609. feature "WDR82" (0.000283)
610. feature "BAG1" (0.000279)
611. feature "LRIF1" (0.000275)
612. feature "TYW3" (0.000274)
613. feature "CRNDE" (0.000267)
614. feature "AP3D1" (0.000267)
615. feature "ADD2" (0.000265)
616. feature "AREG" (0.000264)
617. feature "PLCB4" (0.000264)
618. feature "CHP1" (0.000260)
619. feature "KIF14" (0.000259)
620. feature "NCKAP1" (0.000258)
621. feature "SPIRE1" (0.000254)
622. feature "CLSPN" (0.000252)
623. feature "RALBP1" (0.000251)
624. feature "ZDHHC6" (0.000250)
625. feature "NECAB1" (0.000248)
626. feature "TBCA" (0.000244)
627. feature "CDC37" (0.000243)
628. feature "NAA15" (0.000240)
629. feature "SHOX" (0.000237)
630. feature "ATP1A1-AS1" (0.000237)
631. feature "FBXO42" (0.000236)
632. feature "NFIC" (0.000234)
633. feature "ALDH2" (0.000232)
634. feature "BMS1" (0.000230)
635. feature "PATJ" (0.000229)
636. feature "NSD2" (0.000223)
637. feature "PKP4" (0.000220)
638. feature "CCDC186" (0.000218)
639. feature "NCOA5" (0.000216)
640. feature "POLR2I" (0.000213)
641. feature "NONO" (0.000213)
642. feature "IMPA1" (0.000211)
643. feature "IWS1" (0.000209)
644. feature "KPNA4" (0.000206)
645. feature "ARHGEF11" (0.000203)
646. feature "COPS9" (0.000192)
647. feature "ERF" (0.000192)
648. feature "ZBTB26" (0.000187)
649. feature "SLC24A2" (0.000170)
650. feature "CPEB2" (0.000167)
651. feature "FAM120A" (0.000167)
652. feature "RBBP8" (0.000166)
653. feature "MARK3" (0.000165)
654. feature "TAF6" (0.000165)
655. feature "ARHGAP21" (0.000162)
656. feature "TIMM8B" (0.000159)
657. feature "ZNF347" (0.000158)
658. feature "TTC39A" (0.000148)
659. feature "RARG" (0.000147)
660. feature "SNX27" (0.000147)
661. feature "BDP1" (0.000143)
662. feature "TJAP1" (0.000141)
663. feature "MAPK9" (0.000140)
664. feature "DEDD" (0.000139)
665. feature "CNNM2" (0.000135)
666. feature "PC" (0.000134)
667. feature "CDKN3" (0.000133)
668. feature "PTPN23" (0.000133)
669. feature "RPS6KA4" (0.000129)
670. feature "PATL1" (0.000128)
671. feature "TNFSF13B" (0.000125)
672. feature "LCMT1" (0.000120)
673. feature "TRIM41" (0.000120)
674. feature "TCHP" (0.000119)
675. feature "ATP6V1E1" (0.000117)
676. feature "ZRANB1" (0.000117)
677. feature "FAAP100" (0.000107)
678. feature "BTBD9" (0.000107)
679. feature "TP53BP2" (0.000106)
680. feature "UBP1" (0.000093)
681. feature "DNAJC21" (0.000089)
682. feature "ATP6V1H" (0.000087)
683. feature "CCNI" (0.000084)
684. feature "SNX24" (0.000081)
685. feature "TRIM44" (0.000081)
686. feature "PPP2R5E" (0.000080)
687. feature "MSR1" (0.000077)
688. feature "TNIK" (0.000076)
689. feature "BTBD6" (0.000075)
690. feature "PANK3" (0.000073)
691. feature "RELA" (0.000068)
692. feature "USP24" (0.000065)
693. feature "DNAJC2" (0.000064)
694. feature "LPP" (0.000063)
695. feature "C8orf88" (0.000062)
696. feature "UNC50" (0.000058)
697. feature "LXN" (0.000057)
698. feature "CPSF2" (0.000054)
699. feature "HS6ST1" (0.000050)
700. feature "RRN3P3" (0.000048)
701. feature "SUMO3" (0.000048)
702. feature "OR10K1" (0.000048)
703. feature "METRNL" (0.000048)
704. feature "ATP1A4" (0.000036)
705. feature "UBE2F" (0.000036)
706. feature "CRK" (0.000035)
707. feature "SLC25A43" (0.000035)
708. feature "MAILR" (0.000030)
709. feature "TARDBP" (0.000030)
710. feature "DCAF4" (0.000029)
711. feature "C4orf48" (0.000028)
712. feature "ZCCHC2" (0.000025)
713. feature "ZC3H13" (0.000025)
714. feature "WDR46" (0.000021)
715. feature "PECR" (0.000017)
716. feature "KHSRP" (0.000012)
717. feature "GIT1" (0.000006)
718. feature "HMGA1" (0.000005)
719. feature "MTCH1" (0.000004)
720. feature "SCARA3" (0.000000)
721. feature "TJP3" (0.000000)
722. feature "PCDHGA1" (0.000000)
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724. feature "HMGB1P8" (0.000000)
725. feature "LDLRAP1" (0.000000)
726. feature "RHD" (0.000000)
727. feature "CNR2" (0.000000)
728. feature "SNX8" (0.000000)
729. feature "PHACTR1" (0.000000)
730. feature "CRAMP1" (0.000000)
731. feature "C9orf152" (0.000000)
732. feature "CCDC102A" (0.000000)
733. feature "NOG" (0.000000)
734. feature "WDR5" (0.000000)
735. feature "PIP5K1C" (0.000000)
736. feature "CARM1" (0.000000)
737. feature "GYG2" (0.000000)
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740. feature "HDAC7" (0.000000)
741. feature "SLC52A3" (0.000000)
742. feature "MT-TH" (0.000000)
743. feature "C3orf62" (0.000000)
744. feature "ZYX" (0.000000)
745. feature "EDA" (0.000000)
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750. feature "LINC00639" (0.000000)
751. feature "NXN" (0.000000)
752. feature "CYP19A1" (0.000000)
753. feature "ZNF672" (0.000000)
754. feature "LINC00853" (0.000000)
755. feature "LINC01128" (0.000000)
756. feature "HES6" (0.000000)
757. feature "MAPK8IP2" (0.000000)
758. feature "L3MBTL2-AS1" (0.000000)
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760. feature "EN2" (0.000000)
761. feature "SMPD2" (0.000000)
762. feature "ALKBH8" (0.000000)
763. feature "OPA1" (0.000000)
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765. feature "SUSD3" (0.000000)
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767. feature "MARS2" (0.000000)
768. feature "SAMD4B" (0.000000)
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770. feature "HARBI1" (0.000000)
771. feature "CHST6" (0.000000)
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773. feature "ZNF396" (0.000000)
774. feature "MOB3A" (0.000000)
775. feature "PRRC2A" (0.000000)
776. feature "EMP3" (0.000000)
777. feature "KIF9-AS1" (0.000000)
778. feature "MTCO1P40" (0.000000)
779. feature "MYO9B" (0.000000)
780. feature "KIFC2" (0.000000)
781. feature "PLEKHH3" (0.000000)
782. feature "STAM-DT" (0.000000)
783. feature "ITM2C" (0.000000)
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785. feature "ZFP3" (0.000000)
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796. feature "TAF3" (0.000000)
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830. feature "LINC01355" (0.000000)
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833. feature "GPN1" (0.000000)
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838. feature "KCNJ3" (0.000000)
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840. feature "FBXO30" (0.000000)
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2928. feature "NIBAN2" (0.000000)
2929. feature "EIF2AK2" (0.000000)
2930. feature "PKP1" (0.000000)
2931. feature "ABTB2" (0.000000)
2932. feature "PINLYP" (0.000000)
2933. feature "POTEC" (0.000000)
2934. feature "PMS2CL" (0.000000)
2935. feature "MAP4K4" (0.000000)
2936. feature "ZNF768" (0.000000)
2937. feature "CHMP4B" (0.000000)
2938. feature "H2AX" (0.000000)
2939. feature "FBXL19" (0.000000)
2940. feature "CNIH3" (0.000000)
2941. feature "DCAF10" (0.000000)
2942. feature "HID1" (0.000000)
2943. feature "ELP1" (0.000000)
2944. feature "TDRD5" (0.000000)
2945. feature "ZNF579" (0.000000)
2946. feature "TATDN2" (0.000000)
2947. feature "ACVR1" (0.000000)
2948. feature "LIPT1" (0.000000)
2949. feature "ANKRD42" (0.000000)
2950. feature "BATF" (0.000000)
2951. feature "DPY19L1" (0.000000)
2952. feature "PRKAG1" (0.000000)
2953. feature "SHROOM2" (0.000000)
2954. feature "PCED1A" (0.000000)
2955. feature "LINC02080" (0.000000)
2956. feature "BICD2" (0.000000)
2957. feature "STX3" (0.000000)
2958. feature "LCLAT1" (0.000000)
2959. feature "KCNK6" (0.000000)
2960. feature "ZNF77" (0.000000)
2961. feature "HCG18" (0.000000)
2962. feature "TRIM31" (0.000000)
2963. feature "LSS" (0.000000)
2964. feature "IL1RAP" (0.000000)
2965. feature "PHF1" (0.000000)
2966. feature "NOMO3" (0.000000)
2967. feature "XRRA1" (0.000000)
2968. feature "TMEM164" (0.000000)
2969. feature "WDR5B-DT" (0.000000)
2970. feature "FAM104A" (0.000000)
2971. feature "HAND2-AS1" (0.000000)
2972. feature "NDEL1" (0.000000)
2973. feature "ZDHHC5" (0.000000)
2974. feature "NCAM1" (0.000000)
2975. feature "ATXN7L1" (0.000000)
2976. feature "CNPY4" (0.000000)
2977. feature "LIN54" (0.000000)
2978. feature "MDM1" (0.000000)
2979. feature "PSCA" (0.000000)
2980. feature "VDR" (0.000000)
2981. feature "REV3L" (0.000000)
2982. feature "CS" (0.000000)
2983. feature "TF" (0.000000)
2984. feature "TMEM69" (0.000000)
2985. feature "BCCIP" (0.000000)
2986. feature "CIC" (0.000000)
2987. feature "UBE2E3" (0.000000)
2988. feature "AIMP1P1" (0.000000)
2989. feature "CDK12" (0.000000)
2990. feature "KRT16" (0.000000)
2991. feature "PCNX3" (0.000000)
2992. feature "NEMP1" (0.000000)
2993. feature "XKR4" (0.000000)
2994. feature "STARD7" (0.000000)
2995. feature "TBC1D10A" (0.000000)
2996. feature "CBX6" (0.000000)
2997. feature "SSH3" (0.000000)
2998. feature "ZBED6" (0.000000)
2999. feature "ZZEF1" (0.000000)
3000. feature "PPP1R3D" (0.000000)

We decided moreover to experiment with another classifier.

We decided to try to train an adaptive boosting classifier, that is known for its simplicity and effectiveness.

It trains a sequence of weak learners iteratively. In each iteration, the algorithm assigns weights to the training instances, with higher weights given to instances that were misclassified by the previous weak learners. This focuses the attention of the subsequent weak learners on the difficult-to-classify instances. After each weak learner is trained, AdaBoost assigns a weight to its prediction based on its accuracy. The algorithm then combines the predictions of all weak learners by weighted voting, where the weight of each weak learner's prediction depends on its accuracy.

In the parameters we used the algorithm 'SAMME.R' as it is an extension of SAMME that provides real-valued class probabilities instead of discrete class labels, allowing the model to produce more flexible output.

The problem of this classifier was that it needed a very long time to find the best hyperparameters and to fit the trainig data to finally have literally the same performance as the xgboost classifier (only 0.03% better).

The good news was that also this model confirmed us the importance of the gene MT-RNR2 for the predictions.

So we still decided to keep it in the report but not to use it in the next DropSeq dataset.

In [ ]:
#i want to implement an adaptive boosting classifier for the DropSeq MCF7 dataset to see if it performs better than the random forest and XGBoost classifiers

from sklearn.ensemble import AdaBoostClassifier

# Define the AdaBoost classifier
ada = AdaBoostClassifier(algorithm= 'SAMME' , random_state=42)

# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [100, 150], 'learning_rate': [0.5, 1]}
grid_search = GridSearchCV(ada, param_grid, cv=5)

outer_cv = KFold(n_splits=5, shuffle=True, random_state=42)

# Perform the cross validation
scores = cross_val_score(grid_search, X_train_df_DS_MCF7, y_train_df_DS_MCF7, cv=outer_cv)

# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))

# Perform GridSearchCV on the entire dataset to get the best parameters and fit the model
grid_search.fit(X_train_df_DS_MCF7, y_train_df_DS_MCF7)
print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
best_ada_DS_MCF7 = grid_search.best_estimator_
print("best model: ", best_ada_DS_MCF7)

#i want to test the model on the test dataset
print("Test accuracy: ", best_ada_DS_MCF7.score(X_test_df_DS_MCF7, y_test_df_DS_MCF7))

# Print the feature importances
importances = best_ada_DS_MCF7.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_df_DS_MCF7.shape[1]):
    print("%d. feature %s (%f)" % (f + 1, X_train_df_DS_MCF7.columns[indices[f]], importances[indices[f]]))

Mean accuracy of nested cross validation:  0.972485549132948
Best hyperparameters:  {'learning_rate': 1, 'n_estimators': 150}
Best accuracy:  0.9733526011560694
best model:  AdaBoostClassifier(algorithm='SAMME', learning_rate=1, n_estimators=150,
                   random_state=42)
Test accuracy:  0.9759593157651411
Feature ranking:
1. feature "TMSB10" (0.043231)
2. feature "MT-RNR2" (0.033690)
3. feature "TFF1" (0.030733)
4. feature "MT-RNR1" (0.029706)
5. feature "MALAT1" (0.029389)
6. feature "TPI1" (0.028143)
7. feature "RPL35" (0.025724)
8. feature "NEAT1" (0.025381)
9. feature "MIR663AHG" (0.025306)
10. feature "PGK1" (0.024401)
11. feature "KRT19" (0.023226)
12. feature "BCYRN1" (0.022916)
13. feature "MT-ND3" (0.022755)
14. feature "GAPDH" (0.022508)
15. feature "PKM" (0.021776)
16. feature "MT-ND4" (0.017891)
17. feature "RPL13" (0.017106)
18. feature "ENO1" (0.016967)
19. feature "TOB1" (0.016283)
20. feature "MT-CO3" (0.016189)
21. feature "SET" (0.015024)
22. feature "H4C3" (0.015011)
23. feature "RPS19" (0.014878)
24. feature "MT-CYB" (0.014521)
25. feature "RPL23" (0.013795)
26. feature "LGALS1" (0.012891)
27. feature "DSP" (0.012886)
28. feature "MT-ND2" (0.012753)
29. feature "FGF23" (0.012433)
30. feature "RPS14" (0.011659)
31. feature "HES1" (0.011081)
32. feature "PRRC2C" (0.011047)
33. feature "RPL30" (0.010997)
34. feature "H2AC20" (0.010963)
35. feature "RPL12" (0.010512)
36. feature "PKIB" (0.009693)
37. feature "RPL15" (0.009573)
38. feature "ACTB" (0.009405)
39. feature "MT-ND4L" (0.009283)
40. feature "MT-TS1" (0.009224)
41. feature "TRIM37" (0.009201)
42. feature "TMEM64" (0.008933)
43. feature "TPD52L1" (0.008825)
44. feature "MT-ATP6" (0.008609)
45. feature "RPS5" (0.007764)
46. feature "HDGF" (0.007592)
47. feature "TMEM258" (0.007367)
48. feature "VMP1" (0.007002)
49. feature "RPL35A" (0.006927)
50. feature "SLC9A3R1" (0.006816)
51. feature "BCAS3" (0.006675)
52. feature "PARD6B" (0.006473)
53. feature "NPM1P40" (0.006187)
54. feature "TFF3" (0.006116)
55. feature "GPM6A" (0.006091)
56. feature "RPL34" (0.005995)
57. feature "PAK2" (0.005951)
58. feature "H1-4" (0.005880)
59. feature "WSB2" (0.005740)
60. feature "SNRPD2" (0.005454)
61. feature "CAPZA1" (0.005420)
62. feature "YWHAB" (0.005232)
63. feature "DNAJA1" (0.005192)
64. feature "S100A10" (0.004976)
65. feature "ANP32B" (0.004958)
66. feature "MT-ATP8" (0.004678)
67. feature "GOLGA4" (0.004576)
68. feature "MT-CO2" (0.004437)
69. feature "MT-ND5" (0.004358)
70. feature "HNRNPAB" (0.004358)
71. feature "H2AC11" (0.004310)
72. feature "ZBTB20" (0.004196)
73. feature "NSD1" (0.004066)
74. feature "PFDN4" (0.004045)
75. feature "SNX27" (0.003949)
76. feature "NFIC" (0.003925)
77. feature "PSMA7" (0.003919)
78. feature "SOX4" (0.003866)
79. feature "S100P" (0.003862)
80. feature "HSPD1" (0.003852)
81. feature "RPLP2" (0.003754)
82. feature "MT-ND6" (0.003651)
83. feature "H19" (0.003644)
84. feature "BTN3A2" (0.003621)
85. feature "PROSER1" (0.003567)
86. feature "BAP1" (0.003405)
87. feature "GPATCH4" (0.003391)
88. feature "RPS12" (0.003335)
89. feature "DEK" (0.003091)
90. feature "CSK" (0.002933)
91. feature "SPATS2L" (0.002594)
92. feature "C8orf33" (0.002155)
93. feature "IGFBP5" (0.002132)
94. feature "ZNF799" (0.000000)
95. feature "MCM2" (0.000000)
96. feature "SAMD4B" (0.000000)
97. feature "ITGB8" (0.000000)
98. feature "ABHD16A" (0.000000)
99. feature "PCDHB1" (0.000000)
100. feature "CDR2L" (0.000000)
101. feature "MGAT4A" (0.000000)
102. feature "RFWD3" (0.000000)
103. feature "KAZN" (0.000000)
104. feature "IL18BP" (0.000000)
105. feature "BBS7-DT" (0.000000)
106. feature "FBXL16" (0.000000)
107. feature "NGRN" (0.000000)
108. feature "CCDC81" (0.000000)
109. feature "IL4R" (0.000000)
110. feature "TFE3" (0.000000)
111. feature "NOTCH4" (0.000000)
112. feature "ISCU" (0.000000)
113. feature "TCHP" (0.000000)
114. feature "GASK1B" (0.000000)
115. feature "CCNE1" (0.000000)
116. feature "MYG1-AS1" (0.000000)
117. feature "CLSTN3" (0.000000)
118. feature "GPR137C" (0.000000)
119. feature "TNIK" (0.000000)
120. feature "HMGN1P17" (0.000000)
121. feature "MTARC1" (0.000000)
122. feature "MBD6" (0.000000)
123. feature "TENT2" (0.000000)
124. feature "DROSHA" (0.000000)
125. feature "CACNG4" (0.000000)
126. feature "CCDC51" (0.000000)
127. feature "ZNF142" (0.000000)
128. feature "RABEP1" (0.000000)
129. feature "ITGB8-AS1" (0.000000)
130. feature "TEAD2" (0.000000)
131. feature "RRAGC" (0.000000)
132. feature "SHOX" (0.000000)
133. feature "PTPN23" (0.000000)
134. feature "ATP5ME" (0.000000)
135. feature "ALDH4A1" (0.000000)
136. feature "LINC00885" (0.000000)
137. feature "TJAP1" (0.000000)
138. feature "FAM222A" (0.000000)
139. feature "FOXO6" (0.000000)
140. feature "CAMK2D" (0.000000)
141. feature "TMEM139" (0.000000)
142. feature "SNHG12" (0.000000)
143. feature "CHST6" (0.000000)
144. feature "MFAP3" (0.000000)
145. feature "TRIM69" (0.000000)
146. feature "ZNF571-AS1" (0.000000)
147. feature "TRIM62" (0.000000)
148. feature "UGCG" (0.000000)
149. feature "CCDC18" (0.000000)
150. feature "APOOL" (0.000000)
151. feature "RGP1" (0.000000)
152. feature "HARBI1" (0.000000)
153. feature "OR2I1P" (0.000000)
154. feature "ASB6" (0.000000)
155. feature "ARL6" (0.000000)
156. feature "RFK" (0.000000)
157. feature "CXCL12" (0.000000)
158. feature "LRRC59" (0.000000)
159. feature "MAP3K14" (0.000000)
160. feature "PABPC1L" (0.000000)
161. feature "FAM193B" (0.000000)
162. feature "TGM2" (0.000000)
163. feature "TRIM39" (0.000000)
164. feature "BMS1P23" (0.000000)
165. feature "YTHDF1" (0.000000)
166. feature "POLR3E" (0.000000)
167. feature "EPCAM-DT" (0.000000)
168. feature "INO80E" (0.000000)
169. feature "OXSM" (0.000000)
170. feature "RPSAP48" (0.000000)
171. feature "RUBCN" (0.000000)
172. feature "GRK6" (0.000000)
173. feature "POMK" (0.000000)
174. feature "TCF4" (0.000000)
175. feature "PINK1" (0.000000)
176. feature "EFNB1" (0.000000)
177. feature "SPDEF" (0.000000)
178. feature "DTX3L" (0.000000)
179. feature "AFG3L1P" (0.000000)
180. feature "KLF9" (0.000000)
181. feature "ARHGEF11" (0.000000)
182. feature "KLK6" (0.000000)
183. feature "TTC39A" (0.000000)
184. feature "PAX9" (0.000000)
185. feature "NCOA1" (0.000000)
186. feature "GOLGA8B" (0.000000)
187. feature "FA2H" (0.000000)
188. feature "NOTCH2NLC" (0.000000)
189. feature "PCF11" (0.000000)
190. feature "BCAR1" (0.000000)
191. feature "RBMS3" (0.000000)
192. feature "PAN3" (0.000000)
193. feature "THBS3" (0.000000)
194. feature "SUSD3" (0.000000)
195. feature "EFHD1" (0.000000)
196. feature "LY6E-DT" (0.000000)
197. feature "GMEB2" (0.000000)
198. feature "OTUB2" (0.000000)
199. feature "RPIA" (0.000000)
200. feature "AKAP5" (0.000000)
201. feature "FIZ1" (0.000000)
202. feature "RABGGTB" (0.000000)
203. feature "LYPD6" (0.000000)
204. feature "PPP1R8" (0.000000)
205. feature "SPACA6" (0.000000)
206. feature "ASAH2B" (0.000000)
207. feature "TWNK" (0.000000)
208. feature "MAFF" (0.000000)
209. feature "FOXK2" (0.000000)
210. feature "TTPA" (0.000000)
211. feature "UBE2L6" (0.000000)
212. feature "USP46-DT" (0.000000)
213. feature "SRCAP" (0.000000)
214. feature "IGSF6" (0.000000)
215. feature "CLEC2D" (0.000000)
216. feature "NFKBIZ" (0.000000)
217. feature "ZNF254" (0.000000)
218. feature "MT1F" (0.000000)
219. feature "KCTD2" (0.000000)
220. feature "SH3RF1" (0.000000)
221. feature "NEDD4L" (0.000000)
222. feature "PCED1B-AS1" (0.000000)
223. feature "GDAP2" (0.000000)
224. feature "RPS15" (0.000000)
225. feature "GLIS2" (0.000000)
226. feature "EPN3" (0.000000)
227. feature "WWC3" (0.000000)
228. feature "RPL37P6" (0.000000)
229. feature "USP32" (0.000000)
230. feature "USP38" (0.000000)
231. feature "PABIR2" (0.000000)
232. feature "XRCC2" (0.000000)
233. feature "IGF2BP2" (0.000000)
234. feature "OR9H1P" (0.000000)
235. feature "RBM15B" (0.000000)
236. feature "SDCBP2-AS1" (0.000000)
237. feature "CABLES2" (0.000000)
238. feature "CDKN3" (0.000000)
239. feature "CSDE1" (0.000000)
240. feature "PPP1R9B" (0.000000)
241. feature "SLK" (0.000000)
242. feature "FASN" (0.000000)
243. feature "PTHLH" (0.000000)
244. feature "RPL34P1" (0.000000)
245. feature "HMGB2" (0.000000)
246. feature "FLOT2" (0.000000)
247. feature "ZNF777" (0.000000)
248. feature "RPL12P38" (0.000000)
249. feature "TIMM29" (0.000000)
250. feature "TMEM222" (0.000000)
251. feature "RESF1" (0.000000)
252. feature "MTCL1" (0.000000)
253. feature "PPOX" (0.000000)
254. feature "ROCK1" (0.000000)
255. feature "SYTL2" (0.000000)
256. feature "TUBG2" (0.000000)
257. feature "TNFRSF10B" (0.000000)
258. feature "LINC01355" (0.000000)
259. feature "CCDC18-AS1" (0.000000)
260. feature "ERF" (0.000000)
261. feature "DVL3" (0.000000)
262. feature "THAP1" (0.000000)
263. feature "VEPH1" (0.000000)
264. feature "VASN" (0.000000)
265. feature "PTPN7" (0.000000)
266. feature "MMAA" (0.000000)
267. feature "PARD3B" (0.000000)
268. feature "RBBP8" (0.000000)
269. feature "NES" (0.000000)
270. feature "KCNJ3" (0.000000)
271. feature "ROMO1" (0.000000)
272. feature "CEP162" (0.000000)
273. feature "MOB3A" (0.000000)
274. feature "BACH2" (0.000000)
275. feature "WDR5" (0.000000)
276. feature "ZNF316" (0.000000)
277. feature "CNR2" (0.000000)
278. feature "LDLRAP1" (0.000000)
279. feature "PPP2R3B" (0.000000)
280. feature "OPA1" (0.000000)
281. feature "LINC00352" (0.000000)
282. feature "NFATC2" (0.000000)
283. feature "FBXO30" (0.000000)
284. feature "CDC37" (0.000000)
285. feature "CAPN10-DT" (0.000000)
286. feature "UGDH" (0.000000)
287. feature "FBXO11" (0.000000)
288. feature "SVIP" (0.000000)
289. feature "STRIT1" (0.000000)
290. feature "SLC19A1" (0.000000)
291. feature "DNM3" (0.000000)
292. feature "PRRC2A" (0.000000)
293. feature "TSBP1-AS1" (0.000000)
294. feature "H2BC4" (0.000000)
295. feature "MYO9B" (0.000000)
296. feature "PARVB" (0.000000)
297. feature "AFAP1" (0.000000)
298. feature "KIF26A" (0.000000)
299. feature "CEP85" (0.000000)
300. feature "LRP3" (0.000000)
301. feature "VPS13B-DT" (0.000000)
302. feature "KIF9-AS1" (0.000000)
303. feature "CMBL" (0.000000)
304. feature "XKR6" (0.000000)
305. feature "S100A2" (0.000000)
306. feature "INAFM2" (0.000000)
307. feature "YTHDF3" (0.000000)
308. feature "KIF1C" (0.000000)
309. feature "KIFC2" (0.000000)
310. feature "BICRAL" (0.000000)
311. feature "ZFP3" (0.000000)
312. feature "EPM2A" (0.000000)
313. feature "CENPF" (0.000000)
314. feature "GTF3A" (0.000000)
315. feature "AGAP3" (0.000000)
316. feature "NLK" (0.000000)
317. feature "MMP25-AS1" (0.000000)
318. feature "KLC2" (0.000000)
319. feature "DLG5-AS1" (0.000000)
320. feature "CRACR2B" (0.000000)
321. feature "CLCF1" (0.000000)
322. feature "CPSF4" (0.000000)
323. feature "ZNF354C" (0.000000)
324. feature "STUB1" (0.000000)
325. feature "CHMP1A" (0.000000)
326. feature "IPPK" (0.000000)
327. feature "RAB40C" (0.000000)
328. feature "MAP3K12" (0.000000)
329. feature "LIFR" (0.000000)
330. feature "GHDC" (0.000000)
331. feature "ITGB6" (0.000000)
332. feature "PACS1" (0.000000)
333. feature "ZNF444" (0.000000)
334. feature "GAB2" (0.000000)
335. feature "EMP3" (0.000000)
336. feature "CARM1" (0.000000)
337. feature "EDA" (0.000000)
338. feature "RPS6KB1" (0.000000)
339. feature "SMPD2" (0.000000)
340. feature "SNX24" (0.000000)
341. feature "AARSD1" (0.000000)
342. feature "ETAA1" (0.000000)
343. feature "SMKR1" (0.000000)
344. feature "ZNF180" (0.000000)
345. feature "HELB" (0.000000)
346. feature "ARHGEF34P" (0.000000)
347. feature "RIC1" (0.000000)
348. feature "MTCH1" (0.000000)
349. feature "SPATA33" (0.000000)
350. feature "ZKSCAN8" (0.000000)
351. feature "NCF2" (0.000000)
352. feature "LRRFIP2" (0.000000)
353. feature "JHY" (0.000000)
354. feature "PRR19" (0.000000)
355. feature "SOX9-AS1" (0.000000)
356. feature "NCALD" (0.000000)
357. feature "HSD17B2" (0.000000)
358. feature "NEBL-AS1" (0.000000)
359. feature "S1PR2" (0.000000)
360. feature "HMOX1" (0.000000)
361. feature "MAPKAPK2" (0.000000)
362. feature "RHOH" (0.000000)
363. feature "CLDN15" (0.000000)
364. feature "CSRNP1" (0.000000)
365. feature "DHRS11" (0.000000)
366. feature "ZIC2" (0.000000)
367. feature "PLEKHH3" (0.000000)
368. feature "CIAO3" (0.000000)
369. feature "EYA3" (0.000000)
370. feature "RUNX2" (0.000000)
371. feature "SNORC" (0.000000)
372. feature "MYO3B" (0.000000)
373. feature "FBXL4" (0.000000)
374. feature "NXPH4" (0.000000)
375. feature "NACC1" (0.000000)
376. feature "SYCP3" (0.000000)
377. feature "TSHZ1" (0.000000)
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1911. feature "VDAC1P8" (0.000000)
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1930. feature "KCTD3" (0.000000)
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1932. feature "SIPA1" (0.000000)
1933. feature "RBM23" (0.000000)
1934. feature "TPST1" (0.000000)
1935. feature "MLYCD" (0.000000)
1936. feature "CDKL1" (0.000000)
1937. feature "GSPT1" (0.000000)
1938. feature "BRD7" (0.000000)
1939. feature "EPS8L1" (0.000000)
1940. feature "SLC36A2" (0.000000)
1941. feature "HLCS" (0.000000)
1942. feature "PCDHGA10" (0.000000)
1943. feature "C1orf43" (0.000000)
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1945. feature "SOX7" (0.000000)
1946. feature "GMEB1" (0.000000)
1947. feature "NDUFS6" (0.000000)
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1951. feature "TRAF6" (0.000000)
1952. feature "RAB30" (0.000000)
1953. feature "MAP7" (0.000000)
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1955. feature "POLE" (0.000000)
1956. feature "GCM1" (0.000000)
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1958. feature "GSR" (0.000000)
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1962. feature "NCOA2" (0.000000)
1963. feature "HSPA2" (0.000000)
1964. feature "CHMP6" (0.000000)
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1966. feature "MIB2" (0.000000)
1967. feature "TAF8" (0.000000)
1968. feature "INPP4A" (0.000000)
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1972. feature "ARHGEF4" (0.000000)
1973. feature "BOD1L1" (0.000000)
1974. feature "MRPS34" (0.000000)
1975. feature "TADA2B" (0.000000)
1976. feature "TMPRSS3" (0.000000)
1977. feature "VPS4A" (0.000000)
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1979. feature "CCM2" (0.000000)
1980. feature "ZNF85" (0.000000)
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1985. feature "PABPN1" (0.000000)
1986. feature "CTNNA1-AS1" (0.000000)
1987. feature "ZNF772" (0.000000)
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1990. feature "TMEM212" (0.000000)
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1993. feature "TUBD1" (0.000000)
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1995. feature "YRDC" (0.000000)
1996. feature "TASOR2" (0.000000)
1997. feature "CLEC16A" (0.000000)
1998. feature "KMT2B" (0.000000)
1999. feature "ATRX" (0.000000)
2000. feature "C22orf46" (0.000000)
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2002. feature "SCRG1" (0.000000)
2003. feature "PDE4A" (0.000000)
2004. feature "AUNIP" (0.000000)
2005. feature "RPS19BP1" (0.000000)
2006. feature "BRWD1-AS2" (0.000000)
2007. feature "MYO1F" (0.000000)
2008. feature "RARG" (0.000000)
2009. feature "MICALL1" (0.000000)
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2011. feature "MROH1" (0.000000)
2012. feature "SMIM27" (0.000000)
2013. feature "BCDIN3D" (0.000000)
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2016. feature "GPBP1" (0.000000)
2017. feature "KIAA0753" (0.000000)
2018. feature "ALAD" (0.000000)
2019. feature "USP25" (0.000000)
2020. feature "NETO2" (0.000000)
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2162. feature "TSSC4" (0.000000)
2163. feature "ARHGAP21" (0.000000)
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2167. feature "ENTPD1" (0.000000)
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2198. feature "CCDC144A" (0.000000)
2199. feature "HIP1" (0.000000)
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2207. feature "OTULIN" (0.000000)
2208. feature "MTFR1L" (0.000000)
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2236. feature "SRP68" (0.000000)
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2240. feature "PKP4" (0.000000)
2241. feature "MFSD4A" (0.000000)
2242. feature "TRIM41" (0.000000)
2243. feature "ZNF629" (0.000000)
2244. feature "HNRNPD" (0.000000)
2245. feature "STRIP1" (0.000000)
2246. feature "B3GALT5" (0.000000)
2247. feature "GCLC" (0.000000)
2248. feature "REV1" (0.000000)
2249. feature "ABCD4" (0.000000)
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2251. feature "DYRK1B" (0.000000)
2252. feature "FARSA" (0.000000)
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2254. feature "EPHB4" (0.000000)
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2256. feature "BTRC" (0.000000)
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2260. feature "TMEM69" (0.000000)
2261. feature "CS" (0.000000)
2262. feature "ATP6V1H" (0.000000)
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2872. feature "DEAF1" (0.000000)
2873. feature "NAA10" (0.000000)
2874. feature "PCLO" (0.000000)
2875. feature "MAN1A2" (0.000000)
2876. feature "TMCC1-DT" (0.000000)
2877. feature "SPATA6L" (0.000000)
2878. feature "ITPRID2" (0.000000)
2879. feature "SNHG18" (0.000000)
2880. feature "SCLY" (0.000000)
2881. feature "FAM177A1" (0.000000)
2882. feature "TRAF3IP1" (0.000000)
2883. feature "RCC2" (0.000000)
2884. feature "UBE2J1" (0.000000)
2885. feature "CHAMP1" (0.000000)
2886. feature "LNCARSR" (0.000000)
2887. feature "PAQR7" (0.000000)
2888. feature "HOTAIR" (0.000000)
2889. feature "PLD6" (0.000000)
2890. feature "ZNF217" (0.000000)
2891. feature "TTL" (0.000000)
2892. feature "C6orf132" (0.000000)
2893. feature "LUZP1" (0.000000)
2894. feature "BCL3" (0.000000)
2895. feature "MGAT4B" (0.000000)
2896. feature "CAMK2B" (0.000000)
2897. feature "RPL36AP26" (0.000000)
2898. feature "SMPD4BP" (0.000000)
2899. feature "SRSF1" (0.000000)
2900. feature "CCND3" (0.000000)
2901. feature "KCTD6" (0.000000)
2902. feature "ZDHHC6" (0.000000)
2903. feature "ZNF597" (0.000000)
2904. feature "FAM214B" (0.000000)
2905. feature "KLHL8" (0.000000)
2906. feature "OPTN" (0.000000)
2907. feature "TBP" (0.000000)
2908. feature "URI1" (0.000000)
2909. feature "KRT10" (0.000000)
2910. feature "COL5A1" (0.000000)
2911. feature "GLE1" (0.000000)
2912. feature "NMI" (0.000000)
2913. feature "ZNF354B" (0.000000)
2914. feature "HIRIP3" (0.000000)
2915. feature "MYLK-AS1" (0.000000)
2916. feature "ELAC1" (0.000000)
2917. feature "CALML3-AS1" (0.000000)
2918. feature "RNF122" (0.000000)
2919. feature "DPY19L1P1" (0.000000)
2920. feature "ADD2" (0.000000)
2921. feature "DNAAF11" (0.000000)
2922. feature "KMT5B" (0.000000)
2923. feature "LRFN3" (0.000000)
2924. feature "PCYT1A" (0.000000)
2925. feature "ZNF969P" (0.000000)
2926. feature "PLA2G15" (0.000000)
2927. feature "UBL4A" (0.000000)
2928. feature "FBXW2" (0.000000)
2929. feature "SEC14L2" (0.000000)
2930. feature "CEP78" (0.000000)
2931. feature "HMGB1P51" (0.000000)
2932. feature "NEUROD1" (0.000000)
2933. feature "WWC1" (0.000000)
2934. feature "CPLX1" (0.000000)
2935. feature "HMGN1P14" (0.000000)
2936. feature "NOSTRIN" (0.000000)
2937. feature "LIN52" (0.000000)
2938. feature "LINC02324" (0.000000)
2939. feature "PITPNC1" (0.000000)
2940. feature "VPS52" (0.000000)
2941. feature "EID2B" (0.000000)
2942. feature "BMPR1B" (0.000000)
2943. feature "PAPOLA-DT" (0.000000)
2944. feature "ZFC3H1" (0.000000)
2945. feature "ZNF572" (0.000000)
2946. feature "DDX21" (0.000000)
2947. feature "RPUSD4" (0.000000)
2948. feature "PARP16" (0.000000)
2949. feature "PTGES3L" (0.000000)
2950. feature "CDV3" (0.000000)
2951. feature "CHRNB1" (0.000000)
2952. feature "SEMA4C" (0.000000)
2953. feature "MYO18A" (0.000000)
2954. feature "SPRED3" (0.000000)
2955. feature "ZWILCH" (0.000000)
2956. feature "RAD23B" (0.000000)
2957. feature "ZNF652" (0.000000)
2958. feature "MTMR2" (0.000000)
2959. feature "PPT2" (0.000000)
2960. feature "GDI1" (0.000000)
2961. feature "UNC119" (0.000000)
2962. feature "ARNTL2" (0.000000)
2963. feature "WDR62" (0.000000)
2964. feature "ARHGAP39" (0.000000)
2965. feature "CALML5" (0.000000)
2966. feature "TXNRD2" (0.000000)
2967. feature "FAM118B" (0.000000)
2968. feature "SLC29A2" (0.000000)
2969. feature "TPM3" (0.000000)
2970. feature "CRKL" (0.000000)
2971. feature "C19orf73" (0.000000)
2972. feature "METRNL" (0.000000)
2973. feature "FAM120A" (0.000000)
2974. feature "EIF5B" (0.000000)
2975. feature "HMBS" (0.000000)
2976. feature "SYT12" (0.000000)
2977. feature "PREX1" (0.000000)
2978. feature "C5orf63" (0.000000)
2979. feature "RNF168" (0.000000)
2980. feature "UQCC3" (0.000000)
2981. feature "MAP3K14-AS1" (0.000000)
2982. feature "LYAR" (0.000000)
2983. feature "RAB23" (0.000000)
2984. feature "DNAJC5" (0.000000)
2985. feature "THNSL2" (0.000000)
2986. feature "BAG1" (0.000000)
2987. feature "PKP3" (0.000000)
2988. feature "AVEN" (0.000000)
2989. feature "CA4" (0.000000)
2990. feature "TFEB" (0.000000)
2991. feature "GTF2F1" (0.000000)
2992. feature "ZNF544" (0.000000)
2993. feature "FOXJ3" (0.000000)
2994. feature "EIPR1" (0.000000)
2995. feature "NIBAN3" (0.000000)
2996. feature "OR2AT4" (0.000000)
2997. feature "SH3PXD2A" (0.000000)
2998. feature "EVPL" (0.000000)
2999. feature "TARDBP" (0.000000)
3000. feature "PPP1R3D" (0.000000)

¶

4.3.4) Neural Network

We now define the neural network model to be used with the dataset dropseq MCF7

In [ ]:
# This is the model (neural network) applied to the dataset dropseq MCF7
model_DS_M = NeuralNetworkDropSeq()
model_DS_M
Out[ ]:
NeuralNetworkDropSeq(
  (model): Sequential(
    (0): Flatten(start_dim=1, end_dim=-1)
    (1): Linear(in_features=3000, out_features=512, bias=True)
    (2): LeakyReLU(negative_slope=0.01)
    (3): Dropout(p=0.4, inplace=False)
    (4): Linear(in_features=512, out_features=256, bias=True)
    (5): LeakyReLU(negative_slope=0.01)
    (6): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (7): Dropout(p=0.4, inplace=False)
    (8): Linear(in_features=256, out_features=128, bias=True)
    (9): LeakyReLU(negative_slope=0.01)
    (10): Dropout(p=0.3, inplace=False)
    (11): Linear(in_features=128, out_features=64, bias=True)
    (12): LeakyReLU(negative_slope=0.01)
    (13): Linear(in_features=64, out_features=1, bias=True)
  )
)

We split the train dataset and the labels into train dataset and validation dataset (this second one used to validate and see the accuracy during the training phase). Then we convert these variables into tensors in order to be able to use them with pytorch.

In [ ]:
# convert split dataset in tensors
X_train_DS_MCF7_nn, X_val_DS_MCF7_nn, y_train_DS_MCF7_nn, y_val_DS_MCF7_nn = train_test_split(X_train_df_DS_MCF7.values, y_train_df_DS_MCF7.values, test_size=0.2, random_state=42)


X_train_tensor_M = torch.tensor(X_train_DS_MCF7_nn, dtype=torch.float32)
y_train_tensor_M = torch.tensor(y_train_DS_MCF7_nn, dtype=torch.float32).unsqueeze(1)  # For binary classification
X_val_tensor_M = torch.tensor(X_val_DS_MCF7_nn, dtype=torch.float32)
y_val_tensor_M = torch.tensor(y_val_DS_MCF7_nn, dtype=torch.float32).unsqueeze(1)  # For binary classification
¶

Train and validate the neural network drop-seq MCF7

Here below there is the training process of the neural network using the dataset MCF7 dropseq.

The process involves using Stochastic Gradient Descent (SGD) as the optimizer with a learning rate of 0.01, and the loss function is defined as binary cross-entropy with logits ('BCEWithLogitsLoss') which combines a sigmoid activation and binary cross-entropy loss. The training runs for 1000 epochs.

The training loop starts by setting the model to training mode, resetting the gradients, performing a forward pass on the training data, calculating the training loss, and then using backpropagation to compute the gradients. The optimizer updates the model parameters based on these gradients. Finally, training accuracy is calculated by rounding the sigmoid-activated predictions and comparing them to the true labels.

For validation, the model is set to evaluation mode, and a forward pass is performed on the validation data. The validation loss and accuracy are calculated similarly to the training process but without computing gradients.

In [ ]:
# Train and validate the neural network drop-seq MCF7

optimizer = torch.optim.SGD(model_DS_M.parameters(), lr=0.01)
criterion = nn.BCEWithLogitsLoss() # applies a sigmoid activation to the model's raw logits and calculates the binary cross-entropy loss

epochs = 1000
best_val_acc = 0
patience = 3
no_improve_epochs = 0

best_model_path_M = 'best_model_final_M.pth'

for epoch in range(epochs):
    model_DS_M.train()
    optimizer.zero_grad() # resets the gradients of all model parameters to zero.
    train_output = model_DS_M(X_train_tensor_M)  # Forward pass with all training data, computes the model's predictions (output) for the entire training dataset
    train_loss = criterion(train_output, y_train_tensor_M) # calculates the difference between predicted outputs and actual labels
    train_loss.backward() #computes the gradients of the loss with respect to the model's parameters
    # This uses backpropagation to distribute the error back through the network
    optimizer.step() #The optimizer applies the selected algorithm (like SGD) to update the weights
    
    # Calculate training accuracy
    with torch.no_grad():
        train_predictions = torch.round(torch.sigmoid(train_output))
        train_correct = (train_predictions == y_train_tensor_M).sum().item()
        train_total = y_train_tensor_M.size(0)
        train_acc = train_correct / train_total

    # Validation
    model_DS_M.eval() #  sets the model to evaluation mode, disabling features like dropout and batch normalization to ensure consistency
    with torch.no_grad(): #prevents PyTorch from computing gradients during evaluation.
        val_output = model_DS_M(X_val_tensor_M) #computes predictions on the validation dataset
        val_loss = criterion(val_output, y_val_tensor_M)
        val_predictions = torch.round(torch.sigmoid(val_output))  # converts the logits/predictions into binary predictions (0 or 1)
        val_correct = (val_predictions == y_val_tensor_M).sum().item()
        val_total = y_val_tensor_M.size(0)
        val_acc = val_correct / val_total

    print(f'Epoch {epoch+1}/{epochs}, '
          f'Train Loss: {train_loss.item():.4f}, '
          f'Train Accuracy: {train_acc:.4f}, '
          f'Val Loss: {val_loss.item():.4f}, '
          f'Val Accuracy: {val_acc:.4f}')
    
    

# Save model after all epochs are completed
#torch.save(model.state_dict(), best_model_path)

Epoch 1/1000, Train Loss: 0.7007, Train Accuracy: 0.4471, Val Loss: 0.7040, Val Accuracy: 0.4156
Epoch 2/1000, Train Loss: 0.6994, Train Accuracy: 0.4538, Val Loss: 0.7037, Val Accuracy: 0.4156
Epoch 3/1000, Train Loss: 0.6975, Train Accuracy: 0.4587, Val Loss: 0.7034, Val Accuracy: 0.4156
Epoch 4/1000, Train Loss: 0.6972, Train Accuracy: 0.4642, Val Loss: 0.7032, Val Accuracy: 0.4156
Epoch 5/1000, Train Loss: 0.6952, Train Accuracy: 0.4736, Val Loss: 0.7028, Val Accuracy: 0.4156
Epoch 6/1000, Train Loss: 0.6943, Train Accuracy: 0.4798, Val Loss: 0.7025, Val Accuracy: 0.4156
Epoch 7/1000, Train Loss: 0.6922, Train Accuracy: 0.4956, Val Loss: 0.7022, Val Accuracy: 0.4156
Epoch 8/1000, Train Loss: 0.6915, Train Accuracy: 0.5019, Val Loss: 0.7018, Val Accuracy: 0.4156
Epoch 9/1000, Train Loss: 0.6898, Train Accuracy: 0.5069, Val Loss: 0.7015, Val Accuracy: 0.4156
Epoch 10/1000, Train Loss: 0.6884, Train Accuracy: 0.5208, Val Loss: 0.7011, Val Accuracy: 0.4156
Epoch 11/1000, Train Loss: 0.6859, Train Accuracy: 0.5329, Val Loss: 0.7007, Val Accuracy: 0.4156
Epoch 12/1000, Train Loss: 0.6860, Train Accuracy: 0.5395, Val Loss: 0.7003, Val Accuracy: 0.4156
Epoch 13/1000, Train Loss: 0.6854, Train Accuracy: 0.5392, Val Loss: 0.6999, Val Accuracy: 0.4156
Epoch 14/1000, Train Loss: 0.6836, Train Accuracy: 0.5540, Val Loss: 0.6994, Val Accuracy: 0.4156
Epoch 15/1000, Train Loss: 0.6814, Train Accuracy: 0.5653, Val Loss: 0.6989, Val Accuracy: 0.4156
Epoch 16/1000, Train Loss: 0.6804, Train Accuracy: 0.5756, Val Loss: 0.6984, Val Accuracy: 0.4156
Epoch 17/1000, Train Loss: 0.6787, Train Accuracy: 0.5868, Val Loss: 0.6979, Val Accuracy: 0.4156
Epoch 18/1000, Train Loss: 0.6769, Train Accuracy: 0.6012, Val Loss: 0.6973, Val Accuracy: 0.4156
Epoch 19/1000, Train Loss: 0.6759, Train Accuracy: 0.6089, Val Loss: 0.6968, Val Accuracy: 0.4156
Epoch 20/1000, Train Loss: 0.6745, Train Accuracy: 0.6135, Val Loss: 0.6962, Val Accuracy: 0.4156
Epoch 21/1000, Train Loss: 0.6739, Train Accuracy: 0.6259, Val Loss: 0.6955, Val Accuracy: 0.4156
Epoch 22/1000, Train Loss: 0.6732, Train Accuracy: 0.6305, Val Loss: 0.6948, Val Accuracy: 0.4156
Epoch 23/1000, Train Loss: 0.6715, Train Accuracy: 0.6358, Val Loss: 0.6941, Val Accuracy: 0.4156
Epoch 24/1000, Train Loss: 0.6699, Train Accuracy: 0.6485, Val Loss: 0.6934, Val Accuracy: 0.4156
Epoch 25/1000, Train Loss: 0.6670, Train Accuracy: 0.6625, Val Loss: 0.6926, Val Accuracy: 0.4156
Epoch 26/1000, Train Loss: 0.6665, Train Accuracy: 0.6749, Val Loss: 0.6918, Val Accuracy: 0.4156
Epoch 27/1000, Train Loss: 0.6665, Train Accuracy: 0.6728, Val Loss: 0.6909, Val Accuracy: 0.4159
Epoch 28/1000, Train Loss: 0.6638, Train Accuracy: 0.6887, Val Loss: 0.6900, Val Accuracy: 0.4159
Epoch 29/1000, Train Loss: 0.6629, Train Accuracy: 0.6917, Val Loss: 0.6891, Val Accuracy: 0.4162
Epoch 30/1000, Train Loss: 0.6616, Train Accuracy: 0.6955, Val Loss: 0.6881, Val Accuracy: 0.4168
Epoch 31/1000, Train Loss: 0.6596, Train Accuracy: 0.7027, Val Loss: 0.6871, Val Accuracy: 0.4197
Epoch 32/1000, Train Loss: 0.6593, Train Accuracy: 0.7086, Val Loss: 0.6860, Val Accuracy: 0.4275
Epoch 33/1000, Train Loss: 0.6583, Train Accuracy: 0.7169, Val Loss: 0.6848, Val Accuracy: 0.4425
Epoch 34/1000, Train Loss: 0.6555, Train Accuracy: 0.7290, Val Loss: 0.6836, Val Accuracy: 0.4645
Epoch 35/1000, Train Loss: 0.6548, Train Accuracy: 0.7374, Val Loss: 0.6823, Val Accuracy: 0.4913
Epoch 36/1000, Train Loss: 0.6539, Train Accuracy: 0.7360, Val Loss: 0.6810, Val Accuracy: 0.5240
Epoch 37/1000, Train Loss: 0.6516, Train Accuracy: 0.7486, Val Loss: 0.6796, Val Accuracy: 0.5509
Epoch 38/1000, Train Loss: 0.6493, Train Accuracy: 0.7542, Val Loss: 0.6782, Val Accuracy: 0.5853
Epoch 39/1000, Train Loss: 0.6483, Train Accuracy: 0.7560, Val Loss: 0.6767, Val Accuracy: 0.6185
Epoch 40/1000, Train Loss: 0.6469, Train Accuracy: 0.7647, Val Loss: 0.6751, Val Accuracy: 0.6506
Epoch 41/1000, Train Loss: 0.6458, Train Accuracy: 0.7691, Val Loss: 0.6734, Val Accuracy: 0.6780
Epoch 42/1000, Train Loss: 0.6443, Train Accuracy: 0.7709, Val Loss: 0.6717, Val Accuracy: 0.7006
Epoch 43/1000, Train Loss: 0.6430, Train Accuracy: 0.7762, Val Loss: 0.6699, Val Accuracy: 0.7225
Epoch 44/1000, Train Loss: 0.6421, Train Accuracy: 0.7723, Val Loss: 0.6681, Val Accuracy: 0.7434
Epoch 45/1000, Train Loss: 0.6397, Train Accuracy: 0.7835, Val Loss: 0.6661, Val Accuracy: 0.7601
Epoch 46/1000, Train Loss: 0.6387, Train Accuracy: 0.7876, Val Loss: 0.6641, Val Accuracy: 0.7749
Epoch 47/1000, Train Loss: 0.6377, Train Accuracy: 0.7862, Val Loss: 0.6621, Val Accuracy: 0.7864
Epoch 48/1000, Train Loss: 0.6357, Train Accuracy: 0.7979, Val Loss: 0.6600, Val Accuracy: 0.7980
Epoch 49/1000, Train Loss: 0.6338, Train Accuracy: 0.7978, Val Loss: 0.6579, Val Accuracy: 0.8098
Epoch 50/1000, Train Loss: 0.6328, Train Accuracy: 0.8013, Val Loss: 0.6557, Val Accuracy: 0.8182
Epoch 51/1000, Train Loss: 0.6304, Train Accuracy: 0.8078, Val Loss: 0.6534, Val Accuracy: 0.8225
Epoch 52/1000, Train Loss: 0.6292, Train Accuracy: 0.8045, Val Loss: 0.6511, Val Accuracy: 0.8269
Epoch 53/1000, Train Loss: 0.6277, Train Accuracy: 0.8116, Val Loss: 0.6488, Val Accuracy: 0.8353
Epoch 54/1000, Train Loss: 0.6263, Train Accuracy: 0.8132, Val Loss: 0.6465, Val Accuracy: 0.8390
Epoch 55/1000, Train Loss: 0.6247, Train Accuracy: 0.8127, Val Loss: 0.6441, Val Accuracy: 0.8434
Epoch 56/1000, Train Loss: 0.6239, Train Accuracy: 0.8173, Val Loss: 0.6417, Val Accuracy: 0.8491
Epoch 57/1000, Train Loss: 0.6210, Train Accuracy: 0.8189, Val Loss: 0.6393, Val Accuracy: 0.8549
Epoch 58/1000, Train Loss: 0.6195, Train Accuracy: 0.8258, Val Loss: 0.6369, Val Accuracy: 0.8572
Epoch 59/1000, Train Loss: 0.6177, Train Accuracy: 0.8273, Val Loss: 0.6345, Val Accuracy: 0.8621
Epoch 60/1000, Train Loss: 0.6175, Train Accuracy: 0.8250, Val Loss: 0.6321, Val Accuracy: 0.8639
Epoch 61/1000, Train Loss: 0.6145, Train Accuracy: 0.8294, Val Loss: 0.6297, Val Accuracy: 0.8647
Epoch 62/1000, Train Loss: 0.6129, Train Accuracy: 0.8278, Val Loss: 0.6272, Val Accuracy: 0.8688
Epoch 63/1000, Train Loss: 0.6115, Train Accuracy: 0.8329, Val Loss: 0.6248, Val Accuracy: 0.8697
Epoch 64/1000, Train Loss: 0.6095, Train Accuracy: 0.8335, Val Loss: 0.6224, Val Accuracy: 0.8723
Epoch 65/1000, Train Loss: 0.6069, Train Accuracy: 0.8381, Val Loss: 0.6200, Val Accuracy: 0.8723
Epoch 66/1000, Train Loss: 0.6066, Train Accuracy: 0.8348, Val Loss: 0.6176, Val Accuracy: 0.8734
Epoch 67/1000, Train Loss: 0.6044, Train Accuracy: 0.8421, Val Loss: 0.6152, Val Accuracy: 0.8746
Epoch 68/1000, Train Loss: 0.6023, Train Accuracy: 0.8436, Val Loss: 0.6128, Val Accuracy: 0.8777
Epoch 69/1000, Train Loss: 0.6010, Train Accuracy: 0.8401, Val Loss: 0.6105, Val Accuracy: 0.8789
Epoch 70/1000, Train Loss: 0.5990, Train Accuracy: 0.8450, Val Loss: 0.6081, Val Accuracy: 0.8801
Epoch 71/1000, Train Loss: 0.5964, Train Accuracy: 0.8473, Val Loss: 0.6057, Val Accuracy: 0.8821
Epoch 72/1000, Train Loss: 0.5954, Train Accuracy: 0.8459, Val Loss: 0.6033, Val Accuracy: 0.8835
Epoch 73/1000, Train Loss: 0.5928, Train Accuracy: 0.8512, Val Loss: 0.6009, Val Accuracy: 0.8841
Epoch 74/1000, Train Loss: 0.5918, Train Accuracy: 0.8491, Val Loss: 0.5986, Val Accuracy: 0.8844
Epoch 75/1000, Train Loss: 0.5891, Train Accuracy: 0.8482, Val Loss: 0.5961, Val Accuracy: 0.8853
Epoch 76/1000, Train Loss: 0.5867, Train Accuracy: 0.8540, Val Loss: 0.5937, Val Accuracy: 0.8864
Epoch 77/1000, Train Loss: 0.5851, Train Accuracy: 0.8534, Val Loss: 0.5913, Val Accuracy: 0.8879
Epoch 78/1000, Train Loss: 0.5833, Train Accuracy: 0.8538, Val Loss: 0.5889, Val Accuracy: 0.8890
Epoch 79/1000, Train Loss: 0.5796, Train Accuracy: 0.8590, Val Loss: 0.5866, Val Accuracy: 0.8902
Epoch 80/1000, Train Loss: 0.5778, Train Accuracy: 0.8564, Val Loss: 0.5842, Val Accuracy: 0.8910
Epoch 81/1000, Train Loss: 0.5766, Train Accuracy: 0.8589, Val Loss: 0.5818, Val Accuracy: 0.8913
Epoch 82/1000, Train Loss: 0.5733, Train Accuracy: 0.8576, Val Loss: 0.5793, Val Accuracy: 0.8916
Epoch 83/1000, Train Loss: 0.5714, Train Accuracy: 0.8605, Val Loss: 0.5768, Val Accuracy: 0.8916
Epoch 84/1000, Train Loss: 0.5691, Train Accuracy: 0.8639, Val Loss: 0.5744, Val Accuracy: 0.8922
Epoch 85/1000, Train Loss: 0.5667, Train Accuracy: 0.8639, Val Loss: 0.5719, Val Accuracy: 0.8931
Epoch 86/1000, Train Loss: 0.5643, Train Accuracy: 0.8627, Val Loss: 0.5694, Val Accuracy: 0.8951
Epoch 87/1000, Train Loss: 0.5636, Train Accuracy: 0.8634, Val Loss: 0.5669, Val Accuracy: 0.8954
Epoch 88/1000, Train Loss: 0.5591, Train Accuracy: 0.8678, Val Loss: 0.5644, Val Accuracy: 0.8957
Epoch 89/1000, Train Loss: 0.5579, Train Accuracy: 0.8664, Val Loss: 0.5619, Val Accuracy: 0.8960
Epoch 90/1000, Train Loss: 0.5557, Train Accuracy: 0.8634, Val Loss: 0.5593, Val Accuracy: 0.8971
Epoch 91/1000, Train Loss: 0.5532, Train Accuracy: 0.8702, Val Loss: 0.5567, Val Accuracy: 0.8974
Epoch 92/1000, Train Loss: 0.5505, Train Accuracy: 0.8678, Val Loss: 0.5542, Val Accuracy: 0.8977
Epoch 93/1000, Train Loss: 0.5492, Train Accuracy: 0.8721, Val Loss: 0.5516, Val Accuracy: 0.8980
Epoch 94/1000, Train Loss: 0.5467, Train Accuracy: 0.8722, Val Loss: 0.5490, Val Accuracy: 0.8991
Epoch 95/1000, Train Loss: 0.5428, Train Accuracy: 0.8725, Val Loss: 0.5463, Val Accuracy: 0.8991
Epoch 96/1000, Train Loss: 0.5403, Train Accuracy: 0.8743, Val Loss: 0.5437, Val Accuracy: 0.8994
Epoch 97/1000, Train Loss: 0.5395, Train Accuracy: 0.8743, Val Loss: 0.5411, Val Accuracy: 0.9000
Epoch 98/1000, Train Loss: 0.5350, Train Accuracy: 0.8749, Val Loss: 0.5384, Val Accuracy: 0.9000
Epoch 99/1000, Train Loss: 0.5344, Train Accuracy: 0.8712, Val Loss: 0.5357, Val Accuracy: 0.9003
Epoch 100/1000, Train Loss: 0.5312, Train Accuracy: 0.8745, Val Loss: 0.5329, Val Accuracy: 0.9009
Epoch 101/1000, Train Loss: 0.5289, Train Accuracy: 0.8759, Val Loss: 0.5302, Val Accuracy: 0.9009
Epoch 102/1000, Train Loss: 0.5249, Train Accuracy: 0.8795, Val Loss: 0.5274, Val Accuracy: 0.9014
Epoch 103/1000, Train Loss: 0.5219, Train Accuracy: 0.8806, Val Loss: 0.5245, Val Accuracy: 0.9017
Epoch 104/1000, Train Loss: 0.5202, Train Accuracy: 0.8787, Val Loss: 0.5217, Val Accuracy: 0.9023
Epoch 105/1000, Train Loss: 0.5171, Train Accuracy: 0.8798, Val Loss: 0.5188, Val Accuracy: 0.9032
Epoch 106/1000, Train Loss: 0.5157, Train Accuracy: 0.8788, Val Loss: 0.5159, Val Accuracy: 0.9038
Epoch 107/1000, Train Loss: 0.5118, Train Accuracy: 0.8781, Val Loss: 0.5130, Val Accuracy: 0.9038
Epoch 108/1000, Train Loss: 0.5090, Train Accuracy: 0.8805, Val Loss: 0.5101, Val Accuracy: 0.9035
Epoch 109/1000, Train Loss: 0.5050, Train Accuracy: 0.8848, Val Loss: 0.5071, Val Accuracy: 0.9055
Epoch 110/1000, Train Loss: 0.5030, Train Accuracy: 0.8843, Val Loss: 0.5041, Val Accuracy: 0.9058
Epoch 111/1000, Train Loss: 0.4997, Train Accuracy: 0.8854, Val Loss: 0.5011, Val Accuracy: 0.9064
Epoch 112/1000, Train Loss: 0.4978, Train Accuracy: 0.8864, Val Loss: 0.4980, Val Accuracy: 0.9072
Epoch 113/1000, Train Loss: 0.4949, Train Accuracy: 0.8866, Val Loss: 0.4950, Val Accuracy: 0.9072
Epoch 114/1000, Train Loss: 0.4906, Train Accuracy: 0.8859, Val Loss: 0.4920, Val Accuracy: 0.9072
Epoch 115/1000, Train Loss: 0.4886, Train Accuracy: 0.8871, Val Loss: 0.4889, Val Accuracy: 0.9081
Epoch 116/1000, Train Loss: 0.4866, Train Accuracy: 0.8882, Val Loss: 0.4859, Val Accuracy: 0.9087
Epoch 117/1000, Train Loss: 0.4821, Train Accuracy: 0.8898, Val Loss: 0.4828, Val Accuracy: 0.9087
Epoch 118/1000, Train Loss: 0.4792, Train Accuracy: 0.8908, Val Loss: 0.4797, Val Accuracy: 0.9087
Epoch 119/1000, Train Loss: 0.4760, Train Accuracy: 0.8919, Val Loss: 0.4766, Val Accuracy: 0.9084
Epoch 120/1000, Train Loss: 0.4731, Train Accuracy: 0.8892, Val Loss: 0.4734, Val Accuracy: 0.9087
Epoch 121/1000, Train Loss: 0.4697, Train Accuracy: 0.8915, Val Loss: 0.4702, Val Accuracy: 0.9095
Epoch 122/1000, Train Loss: 0.4663, Train Accuracy: 0.8947, Val Loss: 0.4670, Val Accuracy: 0.9098
Epoch 123/1000, Train Loss: 0.4639, Train Accuracy: 0.8921, Val Loss: 0.4638, Val Accuracy: 0.9110
Epoch 124/1000, Train Loss: 0.4598, Train Accuracy: 0.8957, Val Loss: 0.4606, Val Accuracy: 0.9113
Epoch 125/1000, Train Loss: 0.4577, Train Accuracy: 0.8944, Val Loss: 0.4574, Val Accuracy: 0.9127
Epoch 126/1000, Train Loss: 0.4540, Train Accuracy: 0.8940, Val Loss: 0.4542, Val Accuracy: 0.9127
Epoch 127/1000, Train Loss: 0.4528, Train Accuracy: 0.8960, Val Loss: 0.4509, Val Accuracy: 0.9127
Epoch 128/1000, Train Loss: 0.4479, Train Accuracy: 0.8993, Val Loss: 0.4477, Val Accuracy: 0.9142
Epoch 129/1000, Train Loss: 0.4439, Train Accuracy: 0.8990, Val Loss: 0.4444, Val Accuracy: 0.9145
Epoch 130/1000, Train Loss: 0.4425, Train Accuracy: 0.8974, Val Loss: 0.4412, Val Accuracy: 0.9153
Epoch 131/1000, Train Loss: 0.4391, Train Accuracy: 0.8959, Val Loss: 0.4379, Val Accuracy: 0.9153
Epoch 132/1000, Train Loss: 0.4363, Train Accuracy: 0.8979, Val Loss: 0.4346, Val Accuracy: 0.9171
Epoch 133/1000, Train Loss: 0.4329, Train Accuracy: 0.8995, Val Loss: 0.4312, Val Accuracy: 0.9171
Epoch 134/1000, Train Loss: 0.4292, Train Accuracy: 0.9029, Val Loss: 0.4281, Val Accuracy: 0.9182
Epoch 135/1000, Train Loss: 0.4261, Train Accuracy: 0.9017, Val Loss: 0.4248, Val Accuracy: 0.9185
Epoch 136/1000, Train Loss: 0.4234, Train Accuracy: 0.9001, Val Loss: 0.4215, Val Accuracy: 0.9191
Epoch 137/1000, Train Loss: 0.4200, Train Accuracy: 0.9017, Val Loss: 0.4182, Val Accuracy: 0.9199
Epoch 138/1000, Train Loss: 0.4162, Train Accuracy: 0.9028, Val Loss: 0.4149, Val Accuracy: 0.9199
Epoch 139/1000, Train Loss: 0.4136, Train Accuracy: 0.9022, Val Loss: 0.4116, Val Accuracy: 0.9208
Epoch 140/1000, Train Loss: 0.4094, Train Accuracy: 0.9046, Val Loss: 0.4083, Val Accuracy: 0.9217
Epoch 141/1000, Train Loss: 0.4070, Train Accuracy: 0.9048, Val Loss: 0.4049, Val Accuracy: 0.9220
Epoch 142/1000, Train Loss: 0.4049, Train Accuracy: 0.9031, Val Loss: 0.4016, Val Accuracy: 0.9225
Epoch 143/1000, Train Loss: 0.4015, Train Accuracy: 0.9027, Val Loss: 0.3983, Val Accuracy: 0.9231
Epoch 144/1000, Train Loss: 0.3989, Train Accuracy: 0.9059, Val Loss: 0.3951, Val Accuracy: 0.9231
Epoch 145/1000, Train Loss: 0.3937, Train Accuracy: 0.9092, Val Loss: 0.3918, Val Accuracy: 0.9237
Epoch 146/1000, Train Loss: 0.3917, Train Accuracy: 0.9081, Val Loss: 0.3886, Val Accuracy: 0.9243
Epoch 147/1000, Train Loss: 0.3885, Train Accuracy: 0.9083, Val Loss: 0.3853, Val Accuracy: 0.9249
Epoch 148/1000, Train Loss: 0.3867, Train Accuracy: 0.9087, Val Loss: 0.3820, Val Accuracy: 0.9251
Epoch 149/1000, Train Loss: 0.3827, Train Accuracy: 0.9102, Val Loss: 0.3788, Val Accuracy: 0.9254
Epoch 150/1000, Train Loss: 0.3784, Train Accuracy: 0.9103, Val Loss: 0.3756, Val Accuracy: 0.9254
Epoch 151/1000, Train Loss: 0.3763, Train Accuracy: 0.9114, Val Loss: 0.3725, Val Accuracy: 0.9257
Epoch 152/1000, Train Loss: 0.3724, Train Accuracy: 0.9116, Val Loss: 0.3693, Val Accuracy: 0.9260
Epoch 153/1000, Train Loss: 0.3701, Train Accuracy: 0.9089, Val Loss: 0.3662, Val Accuracy: 0.9263
Epoch 154/1000, Train Loss: 0.3663, Train Accuracy: 0.9130, Val Loss: 0.3631, Val Accuracy: 0.9275
Epoch 155/1000, Train Loss: 0.3647, Train Accuracy: 0.9108, Val Loss: 0.3600, Val Accuracy: 0.9277
Epoch 156/1000, Train Loss: 0.3607, Train Accuracy: 0.9134, Val Loss: 0.3570, Val Accuracy: 0.9286
Epoch 157/1000, Train Loss: 0.3563, Train Accuracy: 0.9133, Val Loss: 0.3539, Val Accuracy: 0.9283
Epoch 158/1000, Train Loss: 0.3545, Train Accuracy: 0.9149, Val Loss: 0.3510, Val Accuracy: 0.9283
Epoch 159/1000, Train Loss: 0.3534, Train Accuracy: 0.9129, Val Loss: 0.3480, Val Accuracy: 0.9292
Epoch 160/1000, Train Loss: 0.3496, Train Accuracy: 0.9135, Val Loss: 0.3450, Val Accuracy: 0.9292
Epoch 161/1000, Train Loss: 0.3478, Train Accuracy: 0.9137, Val Loss: 0.3421, Val Accuracy: 0.9303
Epoch 162/1000, Train Loss: 0.3447, Train Accuracy: 0.9174, Val Loss: 0.3391, Val Accuracy: 0.9309
Epoch 163/1000, Train Loss: 0.3401, Train Accuracy: 0.9173, Val Loss: 0.3361, Val Accuracy: 0.9312
Epoch 164/1000, Train Loss: 0.3386, Train Accuracy: 0.9180, Val Loss: 0.3332, Val Accuracy: 0.9324
Epoch 165/1000, Train Loss: 0.3366, Train Accuracy: 0.9159, Val Loss: 0.3303, Val Accuracy: 0.9327
Epoch 166/1000, Train Loss: 0.3326, Train Accuracy: 0.9199, Val Loss: 0.3274, Val Accuracy: 0.9329
Epoch 167/1000, Train Loss: 0.3303, Train Accuracy: 0.9163, Val Loss: 0.3246, Val Accuracy: 0.9335
Epoch 168/1000, Train Loss: 0.3259, Train Accuracy: 0.9200, Val Loss: 0.3217, Val Accuracy: 0.9344
Epoch 169/1000, Train Loss: 0.3246, Train Accuracy: 0.9219, Val Loss: 0.3190, Val Accuracy: 0.9350
Epoch 170/1000, Train Loss: 0.3231, Train Accuracy: 0.9199, Val Loss: 0.3163, Val Accuracy: 0.9350
Epoch 171/1000, Train Loss: 0.3210, Train Accuracy: 0.9181, Val Loss: 0.3136, Val Accuracy: 0.9353
Epoch 172/1000, Train Loss: 0.3161, Train Accuracy: 0.9225, Val Loss: 0.3108, Val Accuracy: 0.9355
Epoch 173/1000, Train Loss: 0.3130, Train Accuracy: 0.9236, Val Loss: 0.3081, Val Accuracy: 0.9355
Epoch 174/1000, Train Loss: 0.3118, Train Accuracy: 0.9198, Val Loss: 0.3055, Val Accuracy: 0.9364
Epoch 175/1000, Train Loss: 0.3086, Train Accuracy: 0.9246, Val Loss: 0.3029, Val Accuracy: 0.9370
Epoch 176/1000, Train Loss: 0.3075, Train Accuracy: 0.9265, Val Loss: 0.3002, Val Accuracy: 0.9373
Epoch 177/1000, Train Loss: 0.3044, Train Accuracy: 0.9242, Val Loss: 0.2977, Val Accuracy: 0.9376
Epoch 178/1000, Train Loss: 0.3016, Train Accuracy: 0.9244, Val Loss: 0.2952, Val Accuracy: 0.9387
Epoch 179/1000, Train Loss: 0.3003, Train Accuracy: 0.9248, Val Loss: 0.2927, Val Accuracy: 0.9390
Epoch 180/1000, Train Loss: 0.2962, Train Accuracy: 0.9270, Val Loss: 0.2902, Val Accuracy: 0.9393
Epoch 181/1000, Train Loss: 0.2921, Train Accuracy: 0.9244, Val Loss: 0.2878, Val Accuracy: 0.9396
Epoch 182/1000, Train Loss: 0.2924, Train Accuracy: 0.9272, Val Loss: 0.2852, Val Accuracy: 0.9408
Epoch 183/1000, Train Loss: 0.2891, Train Accuracy: 0.9279, Val Loss: 0.2829, Val Accuracy: 0.9402
Epoch 184/1000, Train Loss: 0.2873, Train Accuracy: 0.9270, Val Loss: 0.2805, Val Accuracy: 0.9408
Epoch 185/1000, Train Loss: 0.2852, Train Accuracy: 0.9272, Val Loss: 0.2781, Val Accuracy: 0.9405
Epoch 186/1000, Train Loss: 0.2830, Train Accuracy: 0.9288, Val Loss: 0.2758, Val Accuracy: 0.9408
Epoch 187/1000, Train Loss: 0.2803, Train Accuracy: 0.9275, Val Loss: 0.2736, Val Accuracy: 0.9416
Epoch 188/1000, Train Loss: 0.2784, Train Accuracy: 0.9274, Val Loss: 0.2714, Val Accuracy: 0.9419
Epoch 189/1000, Train Loss: 0.2749, Train Accuracy: 0.9289, Val Loss: 0.2691, Val Accuracy: 0.9422
Epoch 190/1000, Train Loss: 0.2730, Train Accuracy: 0.9307, Val Loss: 0.2669, Val Accuracy: 0.9428
Epoch 191/1000, Train Loss: 0.2717, Train Accuracy: 0.9305, Val Loss: 0.2647, Val Accuracy: 0.9425
Epoch 192/1000, Train Loss: 0.2683, Train Accuracy: 0.9329, Val Loss: 0.2626, Val Accuracy: 0.9425
Epoch 193/1000, Train Loss: 0.2686, Train Accuracy: 0.9311, Val Loss: 0.2604, Val Accuracy: 0.9425
Epoch 194/1000, Train Loss: 0.2643, Train Accuracy: 0.9323, Val Loss: 0.2583, Val Accuracy: 0.9428
Epoch 195/1000, Train Loss: 0.2626, Train Accuracy: 0.9330, Val Loss: 0.2562, Val Accuracy: 0.9428
Epoch 196/1000, Train Loss: 0.2604, Train Accuracy: 0.9331, Val Loss: 0.2541, Val Accuracy: 0.9434
Epoch 197/1000, Train Loss: 0.2601, Train Accuracy: 0.9345, Val Loss: 0.2520, Val Accuracy: 0.9434
Epoch 198/1000, Train Loss: 0.2567, Train Accuracy: 0.9332, Val Loss: 0.2499, Val Accuracy: 0.9434
Epoch 199/1000, Train Loss: 0.2556, Train Accuracy: 0.9329, Val Loss: 0.2480, Val Accuracy: 0.9445
Epoch 200/1000, Train Loss: 0.2527, Train Accuracy: 0.9355, Val Loss: 0.2460, Val Accuracy: 0.9457
Epoch 201/1000, Train Loss: 0.2496, Train Accuracy: 0.9337, Val Loss: 0.2440, Val Accuracy: 0.9457
Epoch 202/1000, Train Loss: 0.2486, Train Accuracy: 0.9358, Val Loss: 0.2421, Val Accuracy: 0.9462
Epoch 203/1000, Train Loss: 0.2475, Train Accuracy: 0.9368, Val Loss: 0.2401, Val Accuracy: 0.9465
Epoch 204/1000, Train Loss: 0.2452, Train Accuracy: 0.9351, Val Loss: 0.2383, Val Accuracy: 0.9457
Epoch 205/1000, Train Loss: 0.2439, Train Accuracy: 0.9340, Val Loss: 0.2365, Val Accuracy: 0.9462
Epoch 206/1000, Train Loss: 0.2415, Train Accuracy: 0.9394, Val Loss: 0.2347, Val Accuracy: 0.9465
Epoch 207/1000, Train Loss: 0.2384, Train Accuracy: 0.9367, Val Loss: 0.2329, Val Accuracy: 0.9465
Epoch 208/1000, Train Loss: 0.2385, Train Accuracy: 0.9382, Val Loss: 0.2311, Val Accuracy: 0.9468
Epoch 209/1000, Train Loss: 0.2354, Train Accuracy: 0.9398, Val Loss: 0.2293, Val Accuracy: 0.9471
Epoch 210/1000, Train Loss: 0.2358, Train Accuracy: 0.9371, Val Loss: 0.2275, Val Accuracy: 0.9474
Epoch 211/1000, Train Loss: 0.2323, Train Accuracy: 0.9404, Val Loss: 0.2258, Val Accuracy: 0.9474
Epoch 212/1000, Train Loss: 0.2313, Train Accuracy: 0.9423, Val Loss: 0.2241, Val Accuracy: 0.9474
Epoch 213/1000, Train Loss: 0.2302, Train Accuracy: 0.9389, Val Loss: 0.2224, Val Accuracy: 0.9474
Epoch 214/1000, Train Loss: 0.2268, Train Accuracy: 0.9400, Val Loss: 0.2207, Val Accuracy: 0.9474
Epoch 215/1000, Train Loss: 0.2261, Train Accuracy: 0.9405, Val Loss: 0.2191, Val Accuracy: 0.9474
Epoch 216/1000, Train Loss: 0.2246, Train Accuracy: 0.9401, Val Loss: 0.2175, Val Accuracy: 0.9474
Epoch 217/1000, Train Loss: 0.2220, Train Accuracy: 0.9434, Val Loss: 0.2159, Val Accuracy: 0.9471
Epoch 218/1000, Train Loss: 0.2204, Train Accuracy: 0.9420, Val Loss: 0.2142, Val Accuracy: 0.9471
Epoch 219/1000, Train Loss: 0.2206, Train Accuracy: 0.9411, Val Loss: 0.2127, Val Accuracy: 0.9471
Epoch 220/1000, Train Loss: 0.2190, Train Accuracy: 0.9410, Val Loss: 0.2111, Val Accuracy: 0.9474
Epoch 221/1000, Train Loss: 0.2162, Train Accuracy: 0.9436, Val Loss: 0.2096, Val Accuracy: 0.9480
Epoch 222/1000, Train Loss: 0.2145, Train Accuracy: 0.9441, Val Loss: 0.2081, Val Accuracy: 0.9480
Epoch 223/1000, Train Loss: 0.2139, Train Accuracy: 0.9413, Val Loss: 0.2066, Val Accuracy: 0.9480
Epoch 224/1000, Train Loss: 0.2142, Train Accuracy: 0.9423, Val Loss: 0.2052, Val Accuracy: 0.9480
Epoch 225/1000, Train Loss: 0.2113, Train Accuracy: 0.9431, Val Loss: 0.2037, Val Accuracy: 0.9480
Epoch 226/1000, Train Loss: 0.2078, Train Accuracy: 0.9438, Val Loss: 0.2023, Val Accuracy: 0.9483
Epoch 227/1000, Train Loss: 0.2077, Train Accuracy: 0.9448, Val Loss: 0.2009, Val Accuracy: 0.9483
Epoch 228/1000, Train Loss: 0.2054, Train Accuracy: 0.9451, Val Loss: 0.1995, Val Accuracy: 0.9488
Epoch 229/1000, Train Loss: 0.2051, Train Accuracy: 0.9452, Val Loss: 0.1981, Val Accuracy: 0.9491
Epoch 230/1000, Train Loss: 0.2030, Train Accuracy: 0.9463, Val Loss: 0.1967, Val Accuracy: 0.9494
Epoch 231/1000, Train Loss: 0.2019, Train Accuracy: 0.9443, Val Loss: 0.1954, Val Accuracy: 0.9494
Epoch 232/1000, Train Loss: 0.2006, Train Accuracy: 0.9454, Val Loss: 0.1941, Val Accuracy: 0.9494
Epoch 233/1000, Train Loss: 0.1986, Train Accuracy: 0.9476, Val Loss: 0.1927, Val Accuracy: 0.9494
Epoch 234/1000, Train Loss: 0.1982, Train Accuracy: 0.9475, Val Loss: 0.1914, Val Accuracy: 0.9497
Epoch 235/1000, Train Loss: 0.1962, Train Accuracy: 0.9468, Val Loss: 0.1902, Val Accuracy: 0.9500
Epoch 236/1000, Train Loss: 0.1946, Train Accuracy: 0.9493, Val Loss: 0.1890, Val Accuracy: 0.9500
Epoch 237/1000, Train Loss: 0.1938, Train Accuracy: 0.9467, Val Loss: 0.1877, Val Accuracy: 0.9506
Epoch 238/1000, Train Loss: 0.1924, Train Accuracy: 0.9483, Val Loss: 0.1865, Val Accuracy: 0.9506
Epoch 239/1000, Train Loss: 0.1912, Train Accuracy: 0.9474, Val Loss: 0.1853, Val Accuracy: 0.9509
Epoch 240/1000, Train Loss: 0.1909, Train Accuracy: 0.9478, Val Loss: 0.1841, Val Accuracy: 0.9512
Epoch 241/1000, Train Loss: 0.1913, Train Accuracy: 0.9488, Val Loss: 0.1830, Val Accuracy: 0.9509
Epoch 242/1000, Train Loss: 0.1881, Train Accuracy: 0.9489, Val Loss: 0.1818, Val Accuracy: 0.9512
Epoch 243/1000, Train Loss: 0.1870, Train Accuracy: 0.9485, Val Loss: 0.1807, Val Accuracy: 0.9512
Epoch 244/1000, Train Loss: 0.1844, Train Accuracy: 0.9519, Val Loss: 0.1795, Val Accuracy: 0.9514
Epoch 245/1000, Train Loss: 0.1860, Train Accuracy: 0.9485, Val Loss: 0.1783, Val Accuracy: 0.9520
Epoch 246/1000, Train Loss: 0.1823, Train Accuracy: 0.9507, Val Loss: 0.1772, Val Accuracy: 0.9526
Epoch 247/1000, Train Loss: 0.1822, Train Accuracy: 0.9493, Val Loss: 0.1761, Val Accuracy: 0.9529
Epoch 248/1000, Train Loss: 0.1809, Train Accuracy: 0.9511, Val Loss: 0.1750, Val Accuracy: 0.9529
Epoch 249/1000, Train Loss: 0.1794, Train Accuracy: 0.9519, Val Loss: 0.1739, Val Accuracy: 0.9532
Epoch 250/1000, Train Loss: 0.1793, Train Accuracy: 0.9504, Val Loss: 0.1728, Val Accuracy: 0.9532
Epoch 251/1000, Train Loss: 0.1776, Train Accuracy: 0.9517, Val Loss: 0.1718, Val Accuracy: 0.9532
Epoch 252/1000, Train Loss: 0.1773, Train Accuracy: 0.9522, Val Loss: 0.1706, Val Accuracy: 0.9535
Epoch 253/1000, Train Loss: 0.1765, Train Accuracy: 0.9504, Val Loss: 0.1696, Val Accuracy: 0.9538
Epoch 254/1000, Train Loss: 0.1739, Train Accuracy: 0.9524, Val Loss: 0.1687, Val Accuracy: 0.9543
Epoch 255/1000, Train Loss: 0.1746, Train Accuracy: 0.9522, Val Loss: 0.1676, Val Accuracy: 0.9546
Epoch 256/1000, Train Loss: 0.1729, Train Accuracy: 0.9525, Val Loss: 0.1666, Val Accuracy: 0.9549
Epoch 257/1000, Train Loss: 0.1704, Train Accuracy: 0.9540, Val Loss: 0.1656, Val Accuracy: 0.9549
Epoch 258/1000, Train Loss: 0.1703, Train Accuracy: 0.9545, Val Loss: 0.1647, Val Accuracy: 0.9555
Epoch 259/1000, Train Loss: 0.1692, Train Accuracy: 0.9520, Val Loss: 0.1637, Val Accuracy: 0.9552
Epoch 260/1000, Train Loss: 0.1684, Train Accuracy: 0.9532, Val Loss: 0.1627, Val Accuracy: 0.9552
Epoch 261/1000, Train Loss: 0.1689, Train Accuracy: 0.9532, Val Loss: 0.1617, Val Accuracy: 0.9555
Epoch 262/1000, Train Loss: 0.1670, Train Accuracy: 0.9532, Val Loss: 0.1608, Val Accuracy: 0.9558
Epoch 263/1000, Train Loss: 0.1631, Train Accuracy: 0.9546, Val Loss: 0.1599, Val Accuracy: 0.9564
Epoch 264/1000, Train Loss: 0.1641, Train Accuracy: 0.9547, Val Loss: 0.1590, Val Accuracy: 0.9564
Epoch 265/1000, Train Loss: 0.1631, Train Accuracy: 0.9557, Val Loss: 0.1581, Val Accuracy: 0.9564
Epoch 266/1000, Train Loss: 0.1633, Train Accuracy: 0.9558, Val Loss: 0.1571, Val Accuracy: 0.9561
Epoch 267/1000, Train Loss: 0.1616, Train Accuracy: 0.9559, Val Loss: 0.1562, Val Accuracy: 0.9561
Epoch 268/1000, Train Loss: 0.1616, Train Accuracy: 0.9556, Val Loss: 0.1554, Val Accuracy: 0.9561
Epoch 269/1000, Train Loss: 0.1588, Train Accuracy: 0.9571, Val Loss: 0.1544, Val Accuracy: 0.9561
Epoch 270/1000, Train Loss: 0.1579, Train Accuracy: 0.9562, Val Loss: 0.1536, Val Accuracy: 0.9561
Epoch 271/1000, Train Loss: 0.1578, Train Accuracy: 0.9568, Val Loss: 0.1527, Val Accuracy: 0.9561
Epoch 272/1000, Train Loss: 0.1575, Train Accuracy: 0.9553, Val Loss: 0.1520, Val Accuracy: 0.9564
Epoch 273/1000, Train Loss: 0.1559, Train Accuracy: 0.9574, Val Loss: 0.1511, Val Accuracy: 0.9564
Epoch 274/1000, Train Loss: 0.1554, Train Accuracy: 0.9561, Val Loss: 0.1503, Val Accuracy: 0.9564
Epoch 275/1000, Train Loss: 0.1526, Train Accuracy: 0.9587, Val Loss: 0.1495, Val Accuracy: 0.9564
Epoch 276/1000, Train Loss: 0.1525, Train Accuracy: 0.9579, Val Loss: 0.1487, Val Accuracy: 0.9566
Epoch 277/1000, Train Loss: 0.1542, Train Accuracy: 0.9582, Val Loss: 0.1479, Val Accuracy: 0.9569
Epoch 278/1000, Train Loss: 0.1520, Train Accuracy: 0.9579, Val Loss: 0.1470, Val Accuracy: 0.9572
Epoch 279/1000, Train Loss: 0.1516, Train Accuracy: 0.9565, Val Loss: 0.1463, Val Accuracy: 0.9578
Epoch 280/1000, Train Loss: 0.1521, Train Accuracy: 0.9565, Val Loss: 0.1455, Val Accuracy: 0.9578
Epoch 281/1000, Train Loss: 0.1499, Train Accuracy: 0.9585, Val Loss: 0.1448, Val Accuracy: 0.9578
Epoch 282/1000, Train Loss: 0.1493, Train Accuracy: 0.9592, Val Loss: 0.1440, Val Accuracy: 0.9578
Epoch 283/1000, Train Loss: 0.1473, Train Accuracy: 0.9586, Val Loss: 0.1432, Val Accuracy: 0.9578
Epoch 284/1000, Train Loss: 0.1486, Train Accuracy: 0.9570, Val Loss: 0.1425, Val Accuracy: 0.9578
Epoch 285/1000, Train Loss: 0.1482, Train Accuracy: 0.9590, Val Loss: 0.1418, Val Accuracy: 0.9581
Epoch 286/1000, Train Loss: 0.1457, Train Accuracy: 0.9591, Val Loss: 0.1411, Val Accuracy: 0.9584
Epoch 287/1000, Train Loss: 0.1463, Train Accuracy: 0.9581, Val Loss: 0.1403, Val Accuracy: 0.9584
Epoch 288/1000, Train Loss: 0.1448, Train Accuracy: 0.9579, Val Loss: 0.1397, Val Accuracy: 0.9584
Epoch 289/1000, Train Loss: 0.1437, Train Accuracy: 0.9590, Val Loss: 0.1390, Val Accuracy: 0.9584
Epoch 290/1000, Train Loss: 0.1417, Train Accuracy: 0.9613, Val Loss: 0.1384, Val Accuracy: 0.9587
Epoch 291/1000, Train Loss: 0.1431, Train Accuracy: 0.9600, Val Loss: 0.1377, Val Accuracy: 0.9590
Epoch 292/1000, Train Loss: 0.1410, Train Accuracy: 0.9619, Val Loss: 0.1371, Val Accuracy: 0.9592
Epoch 293/1000, Train Loss: 0.1412, Train Accuracy: 0.9600, Val Loss: 0.1364, Val Accuracy: 0.9595
Epoch 294/1000, Train Loss: 0.1397, Train Accuracy: 0.9607, Val Loss: 0.1356, Val Accuracy: 0.9595
Epoch 295/1000, Train Loss: 0.1388, Train Accuracy: 0.9616, Val Loss: 0.1350, Val Accuracy: 0.9598
Epoch 296/1000, Train Loss: 0.1382, Train Accuracy: 0.9611, Val Loss: 0.1343, Val Accuracy: 0.9598
Epoch 297/1000, Train Loss: 0.1368, Train Accuracy: 0.9618, Val Loss: 0.1336, Val Accuracy: 0.9601
Epoch 298/1000, Train Loss: 0.1388, Train Accuracy: 0.9605, Val Loss: 0.1330, Val Accuracy: 0.9607
Epoch 299/1000, Train Loss: 0.1387, Train Accuracy: 0.9608, Val Loss: 0.1324, Val Accuracy: 0.9613
Epoch 300/1000, Train Loss: 0.1379, Train Accuracy: 0.9617, Val Loss: 0.1319, Val Accuracy: 0.9610
Epoch 301/1000, Train Loss: 0.1353, Train Accuracy: 0.9598, Val Loss: 0.1314, Val Accuracy: 0.9610
Epoch 302/1000, Train Loss: 0.1352, Train Accuracy: 0.9604, Val Loss: 0.1307, Val Accuracy: 0.9613
Epoch 303/1000, Train Loss: 0.1339, Train Accuracy: 0.9607, Val Loss: 0.1301, Val Accuracy: 0.9616
Epoch 304/1000, Train Loss: 0.1354, Train Accuracy: 0.9603, Val Loss: 0.1295, Val Accuracy: 0.9616
Epoch 305/1000, Train Loss: 0.1331, Train Accuracy: 0.9625, Val Loss: 0.1289, Val Accuracy: 0.9618
Epoch 306/1000, Train Loss: 0.1322, Train Accuracy: 0.9636, Val Loss: 0.1283, Val Accuracy: 0.9618
Epoch 307/1000, Train Loss: 0.1328, Train Accuracy: 0.9611, Val Loss: 0.1278, Val Accuracy: 0.9616
Epoch 308/1000, Train Loss: 0.1315, Train Accuracy: 0.9614, Val Loss: 0.1272, Val Accuracy: 0.9616
Epoch 309/1000, Train Loss: 0.1304, Train Accuracy: 0.9645, Val Loss: 0.1265, Val Accuracy: 0.9618
Epoch 310/1000, Train Loss: 0.1297, Train Accuracy: 0.9629, Val Loss: 0.1259, Val Accuracy: 0.9618
Epoch 311/1000, Train Loss: 0.1303, Train Accuracy: 0.9619, Val Loss: 0.1252, Val Accuracy: 0.9618
Epoch 312/1000, Train Loss: 0.1273, Train Accuracy: 0.9645, Val Loss: 0.1247, Val Accuracy: 0.9621
Epoch 313/1000, Train Loss: 0.1286, Train Accuracy: 0.9619, Val Loss: 0.1241, Val Accuracy: 0.9624
Epoch 314/1000, Train Loss: 0.1284, Train Accuracy: 0.9626, Val Loss: 0.1237, Val Accuracy: 0.9627
Epoch 315/1000, Train Loss: 0.1277, Train Accuracy: 0.9635, Val Loss: 0.1231, Val Accuracy: 0.9627
Epoch 316/1000, Train Loss: 0.1286, Train Accuracy: 0.9640, Val Loss: 0.1225, Val Accuracy: 0.9627
Epoch 317/1000, Train Loss: 0.1275, Train Accuracy: 0.9626, Val Loss: 0.1220, Val Accuracy: 0.9627
Epoch 318/1000, Train Loss: 0.1269, Train Accuracy: 0.9629, Val Loss: 0.1216, Val Accuracy: 0.9627
Epoch 319/1000, Train Loss: 0.1241, Train Accuracy: 0.9650, Val Loss: 0.1211, Val Accuracy: 0.9627
Epoch 320/1000, Train Loss: 0.1240, Train Accuracy: 0.9639, Val Loss: 0.1206, Val Accuracy: 0.9627
Epoch 321/1000, Train Loss: 0.1258, Train Accuracy: 0.9632, Val Loss: 0.1201, Val Accuracy: 0.9627
Epoch 322/1000, Train Loss: 0.1238, Train Accuracy: 0.9650, Val Loss: 0.1196, Val Accuracy: 0.9630
Epoch 323/1000, Train Loss: 0.1242, Train Accuracy: 0.9639, Val Loss: 0.1192, Val Accuracy: 0.9630
Epoch 324/1000, Train Loss: 0.1199, Train Accuracy: 0.9649, Val Loss: 0.1186, Val Accuracy: 0.9627
Epoch 325/1000, Train Loss: 0.1220, Train Accuracy: 0.9650, Val Loss: 0.1182, Val Accuracy: 0.9627
Epoch 326/1000, Train Loss: 0.1229, Train Accuracy: 0.9646, Val Loss: 0.1177, Val Accuracy: 0.9630
Epoch 327/1000, Train Loss: 0.1227, Train Accuracy: 0.9641, Val Loss: 0.1172, Val Accuracy: 0.9630
Epoch 328/1000, Train Loss: 0.1197, Train Accuracy: 0.9652, Val Loss: 0.1167, Val Accuracy: 0.9627
Epoch 329/1000, Train Loss: 0.1203, Train Accuracy: 0.9654, Val Loss: 0.1163, Val Accuracy: 0.9630
Epoch 330/1000, Train Loss: 0.1212, Train Accuracy: 0.9638, Val Loss: 0.1158, Val Accuracy: 0.9633
Epoch 331/1000, Train Loss: 0.1205, Train Accuracy: 0.9649, Val Loss: 0.1153, Val Accuracy: 0.9642
Epoch 332/1000, Train Loss: 0.1181, Train Accuracy: 0.9657, Val Loss: 0.1148, Val Accuracy: 0.9642
Epoch 333/1000, Train Loss: 0.1192, Train Accuracy: 0.9639, Val Loss: 0.1144, Val Accuracy: 0.9645
Epoch 334/1000, Train Loss: 0.1161, Train Accuracy: 0.9673, Val Loss: 0.1140, Val Accuracy: 0.9645
Epoch 335/1000, Train Loss: 0.1166, Train Accuracy: 0.9659, Val Loss: 0.1136, Val Accuracy: 0.9645
Epoch 336/1000, Train Loss: 0.1166, Train Accuracy: 0.9647, Val Loss: 0.1132, Val Accuracy: 0.9645
Epoch 337/1000, Train Loss: 0.1155, Train Accuracy: 0.9665, Val Loss: 0.1128, Val Accuracy: 0.9645
Epoch 338/1000, Train Loss: 0.1148, Train Accuracy: 0.9667, Val Loss: 0.1124, Val Accuracy: 0.9642
Epoch 339/1000, Train Loss: 0.1165, Train Accuracy: 0.9668, Val Loss: 0.1119, Val Accuracy: 0.9645
Epoch 340/1000, Train Loss: 0.1147, Train Accuracy: 0.9670, Val Loss: 0.1115, Val Accuracy: 0.9650
Epoch 341/1000, Train Loss: 0.1164, Train Accuracy: 0.9653, Val Loss: 0.1109, Val Accuracy: 0.9647
Epoch 342/1000, Train Loss: 0.1139, Train Accuracy: 0.9678, Val Loss: 0.1107, Val Accuracy: 0.9650
Epoch 343/1000, Train Loss: 0.1126, Train Accuracy: 0.9663, Val Loss: 0.1102, Val Accuracy: 0.9650
Epoch 344/1000, Train Loss: 0.1136, Train Accuracy: 0.9664, Val Loss: 0.1098, Val Accuracy: 0.9650
Epoch 345/1000, Train Loss: 0.1147, Train Accuracy: 0.9659, Val Loss: 0.1094, Val Accuracy: 0.9650
Epoch 346/1000, Train Loss: 0.1132, Train Accuracy: 0.9665, Val Loss: 0.1090, Val Accuracy: 0.9653
Epoch 347/1000, Train Loss: 0.1105, Train Accuracy: 0.9681, Val Loss: 0.1086, Val Accuracy: 0.9653
Epoch 348/1000, Train Loss: 0.1111, Train Accuracy: 0.9676, Val Loss: 0.1083, Val Accuracy: 0.9659
Epoch 349/1000, Train Loss: 0.1105, Train Accuracy: 0.9677, Val Loss: 0.1079, Val Accuracy: 0.9659
Epoch 350/1000, Train Loss: 0.1114, Train Accuracy: 0.9674, Val Loss: 0.1074, Val Accuracy: 0.9659
Epoch 351/1000, Train Loss: 0.1097, Train Accuracy: 0.9671, Val Loss: 0.1071, Val Accuracy: 0.9659
Epoch 352/1000, Train Loss: 0.1128, Train Accuracy: 0.9663, Val Loss: 0.1068, Val Accuracy: 0.9659
Epoch 353/1000, Train Loss: 0.1098, Train Accuracy: 0.9677, Val Loss: 0.1066, Val Accuracy: 0.9662
Epoch 354/1000, Train Loss: 0.1103, Train Accuracy: 0.9676, Val Loss: 0.1062, Val Accuracy: 0.9668
Epoch 355/1000, Train Loss: 0.1083, Train Accuracy: 0.9675, Val Loss: 0.1058, Val Accuracy: 0.9668
Epoch 356/1000, Train Loss: 0.1092, Train Accuracy: 0.9676, Val Loss: 0.1054, Val Accuracy: 0.9662
Epoch 357/1000, Train Loss: 0.1066, Train Accuracy: 0.9681, Val Loss: 0.1050, Val Accuracy: 0.9665
Epoch 358/1000, Train Loss: 0.1076, Train Accuracy: 0.9682, Val Loss: 0.1047, Val Accuracy: 0.9665
Epoch 359/1000, Train Loss: 0.1056, Train Accuracy: 0.9684, Val Loss: 0.1044, Val Accuracy: 0.9665
Epoch 360/1000, Train Loss: 0.1062, Train Accuracy: 0.9685, Val Loss: 0.1040, Val Accuracy: 0.9665
Epoch 361/1000, Train Loss: 0.1081, Train Accuracy: 0.9673, Val Loss: 0.1037, Val Accuracy: 0.9671
Epoch 362/1000, Train Loss: 0.1071, Train Accuracy: 0.9675, Val Loss: 0.1033, Val Accuracy: 0.9673
Epoch 363/1000, Train Loss: 0.1046, Train Accuracy: 0.9689, Val Loss: 0.1030, Val Accuracy: 0.9676
Epoch 364/1000, Train Loss: 0.1037, Train Accuracy: 0.9706, Val Loss: 0.1026, Val Accuracy: 0.9676
Epoch 365/1000, Train Loss: 0.1046, Train Accuracy: 0.9687, Val Loss: 0.1023, Val Accuracy: 0.9676
Epoch 366/1000, Train Loss: 0.1051, Train Accuracy: 0.9687, Val Loss: 0.1019, Val Accuracy: 0.9676
Epoch 367/1000, Train Loss: 0.1040, Train Accuracy: 0.9688, Val Loss: 0.1015, Val Accuracy: 0.9679
Epoch 368/1000, Train Loss: 0.1031, Train Accuracy: 0.9695, Val Loss: 0.1014, Val Accuracy: 0.9676
Epoch 369/1000, Train Loss: 0.1027, Train Accuracy: 0.9689, Val Loss: 0.1010, Val Accuracy: 0.9676
Epoch 370/1000, Train Loss: 0.1041, Train Accuracy: 0.9693, Val Loss: 0.1007, Val Accuracy: 0.9679
Epoch 371/1000, Train Loss: 0.1038, Train Accuracy: 0.9694, Val Loss: 0.1005, Val Accuracy: 0.9679
Epoch 372/1000, Train Loss: 0.1023, Train Accuracy: 0.9696, Val Loss: 0.1002, Val Accuracy: 0.9685
Epoch 373/1000, Train Loss: 0.1022, Train Accuracy: 0.9704, Val Loss: 0.0998, Val Accuracy: 0.9682
Epoch 374/1000, Train Loss: 0.1015, Train Accuracy: 0.9710, Val Loss: 0.0995, Val Accuracy: 0.9679
Epoch 375/1000, Train Loss: 0.1015, Train Accuracy: 0.9704, Val Loss: 0.0993, Val Accuracy: 0.9679
Epoch 376/1000, Train Loss: 0.1012, Train Accuracy: 0.9697, Val Loss: 0.0990, Val Accuracy: 0.9682
Epoch 377/1000, Train Loss: 0.1006, Train Accuracy: 0.9698, Val Loss: 0.0986, Val Accuracy: 0.9679
Epoch 378/1000, Train Loss: 0.1015, Train Accuracy: 0.9686, Val Loss: 0.0985, Val Accuracy: 0.9682
Epoch 379/1000, Train Loss: 0.1004, Train Accuracy: 0.9702, Val Loss: 0.0981, Val Accuracy: 0.9682
Epoch 380/1000, Train Loss: 0.0999, Train Accuracy: 0.9693, Val Loss: 0.0978, Val Accuracy: 0.9685
Epoch 381/1000, Train Loss: 0.0988, Train Accuracy: 0.9712, Val Loss: 0.0976, Val Accuracy: 0.9682
Epoch 382/1000, Train Loss: 0.1006, Train Accuracy: 0.9696, Val Loss: 0.0972, Val Accuracy: 0.9682
Epoch 383/1000, Train Loss: 0.1004, Train Accuracy: 0.9694, Val Loss: 0.0970, Val Accuracy: 0.9685
Epoch 384/1000, Train Loss: 0.0984, Train Accuracy: 0.9704, Val Loss: 0.0967, Val Accuracy: 0.9688
Epoch 385/1000, Train Loss: 0.0985, Train Accuracy: 0.9717, Val Loss: 0.0965, Val Accuracy: 0.9682
Epoch 386/1000, Train Loss: 0.0991, Train Accuracy: 0.9696, Val Loss: 0.0962, Val Accuracy: 0.9685
Epoch 387/1000, Train Loss: 0.0980, Train Accuracy: 0.9703, Val Loss: 0.0960, Val Accuracy: 0.9682
Epoch 388/1000, Train Loss: 0.0975, Train Accuracy: 0.9705, Val Loss: 0.0957, Val Accuracy: 0.9688
Epoch 389/1000, Train Loss: 0.0969, Train Accuracy: 0.9716, Val Loss: 0.0956, Val Accuracy: 0.9688
Epoch 390/1000, Train Loss: 0.0962, Train Accuracy: 0.9708, Val Loss: 0.0953, Val Accuracy: 0.9691
Epoch 391/1000, Train Loss: 0.0986, Train Accuracy: 0.9686, Val Loss: 0.0952, Val Accuracy: 0.9691
Epoch 392/1000, Train Loss: 0.0966, Train Accuracy: 0.9710, Val Loss: 0.0948, Val Accuracy: 0.9691
Epoch 393/1000, Train Loss: 0.0970, Train Accuracy: 0.9717, Val Loss: 0.0945, Val Accuracy: 0.9691
Epoch 394/1000, Train Loss: 0.0953, Train Accuracy: 0.9714, Val Loss: 0.0942, Val Accuracy: 0.9691
Epoch 395/1000, Train Loss: 0.0956, Train Accuracy: 0.9727, Val Loss: 0.0939, Val Accuracy: 0.9694
Epoch 396/1000, Train Loss: 0.0958, Train Accuracy: 0.9724, Val Loss: 0.0937, Val Accuracy: 0.9694
Epoch 397/1000, Train Loss: 0.0948, Train Accuracy: 0.9715, Val Loss: 0.0934, Val Accuracy: 0.9694
Epoch 398/1000, Train Loss: 0.0949, Train Accuracy: 0.9704, Val Loss: 0.0931, Val Accuracy: 0.9694
Epoch 399/1000, Train Loss: 0.0960, Train Accuracy: 0.9700, Val Loss: 0.0929, Val Accuracy: 0.9697
Epoch 400/1000, Train Loss: 0.0936, Train Accuracy: 0.9733, Val Loss: 0.0926, Val Accuracy: 0.9697
Epoch 401/1000, Train Loss: 0.0953, Train Accuracy: 0.9715, Val Loss: 0.0923, Val Accuracy: 0.9697
Epoch 402/1000, Train Loss: 0.0934, Train Accuracy: 0.9726, Val Loss: 0.0921, Val Accuracy: 0.9697
Epoch 403/1000, Train Loss: 0.0959, Train Accuracy: 0.9712, Val Loss: 0.0919, Val Accuracy: 0.9697
Epoch 404/1000, Train Loss: 0.0925, Train Accuracy: 0.9721, Val Loss: 0.0917, Val Accuracy: 0.9697
Epoch 405/1000, Train Loss: 0.0926, Train Accuracy: 0.9720, Val Loss: 0.0915, Val Accuracy: 0.9694
Epoch 406/1000, Train Loss: 0.0918, Train Accuracy: 0.9722, Val Loss: 0.0912, Val Accuracy: 0.9697
Epoch 407/1000, Train Loss: 0.0937, Train Accuracy: 0.9711, Val Loss: 0.0910, Val Accuracy: 0.9699
Epoch 408/1000, Train Loss: 0.0929, Train Accuracy: 0.9707, Val Loss: 0.0909, Val Accuracy: 0.9697
Epoch 409/1000, Train Loss: 0.0932, Train Accuracy: 0.9717, Val Loss: 0.0906, Val Accuracy: 0.9697
Epoch 410/1000, Train Loss: 0.0909, Train Accuracy: 0.9732, Val Loss: 0.0905, Val Accuracy: 0.9697
Epoch 411/1000, Train Loss: 0.0920, Train Accuracy: 0.9713, Val Loss: 0.0902, Val Accuracy: 0.9697
Epoch 412/1000, Train Loss: 0.0918, Train Accuracy: 0.9727, Val Loss: 0.0900, Val Accuracy: 0.9699
Epoch 413/1000, Train Loss: 0.0918, Train Accuracy: 0.9708, Val Loss: 0.0899, Val Accuracy: 0.9697
Epoch 414/1000, Train Loss: 0.0920, Train Accuracy: 0.9720, Val Loss: 0.0895, Val Accuracy: 0.9697
Epoch 415/1000, Train Loss: 0.0911, Train Accuracy: 0.9728, Val Loss: 0.0893, Val Accuracy: 0.9697
Epoch 416/1000, Train Loss: 0.0912, Train Accuracy: 0.9710, Val Loss: 0.0892, Val Accuracy: 0.9699
Epoch 417/1000, Train Loss: 0.0902, Train Accuracy: 0.9723, Val Loss: 0.0891, Val Accuracy: 0.9697
Epoch 418/1000, Train Loss: 0.0900, Train Accuracy: 0.9719, Val Loss: 0.0891, Val Accuracy: 0.9694
Epoch 419/1000, Train Loss: 0.0898, Train Accuracy: 0.9730, Val Loss: 0.0888, Val Accuracy: 0.9694
Epoch 420/1000, Train Loss: 0.0891, Train Accuracy: 0.9732, Val Loss: 0.0885, Val Accuracy: 0.9697
Epoch 421/1000, Train Loss: 0.0881, Train Accuracy: 0.9728, Val Loss: 0.0882, Val Accuracy: 0.9697
Epoch 422/1000, Train Loss: 0.0889, Train Accuracy: 0.9730, Val Loss: 0.0879, Val Accuracy: 0.9694
Epoch 423/1000, Train Loss: 0.0873, Train Accuracy: 0.9738, Val Loss: 0.0877, Val Accuracy: 0.9691
Epoch 424/1000, Train Loss: 0.0882, Train Accuracy: 0.9732, Val Loss: 0.0875, Val Accuracy: 0.9691
Epoch 425/1000, Train Loss: 0.0883, Train Accuracy: 0.9730, Val Loss: 0.0872, Val Accuracy: 0.9691
Epoch 426/1000, Train Loss: 0.0874, Train Accuracy: 0.9735, Val Loss: 0.0871, Val Accuracy: 0.9691
Epoch 427/1000, Train Loss: 0.0871, Train Accuracy: 0.9735, Val Loss: 0.0869, Val Accuracy: 0.9691
Epoch 428/1000, Train Loss: 0.0881, Train Accuracy: 0.9729, Val Loss: 0.0867, Val Accuracy: 0.9694
Epoch 429/1000, Train Loss: 0.0877, Train Accuracy: 0.9725, Val Loss: 0.0866, Val Accuracy: 0.9697
Epoch 430/1000, Train Loss: 0.0877, Train Accuracy: 0.9738, Val Loss: 0.0865, Val Accuracy: 0.9699
Epoch 431/1000, Train Loss: 0.0865, Train Accuracy: 0.9731, Val Loss: 0.0864, Val Accuracy: 0.9697
Epoch 432/1000, Train Loss: 0.0869, Train Accuracy: 0.9717, Val Loss: 0.0862, Val Accuracy: 0.9697
Epoch 433/1000, Train Loss: 0.0866, Train Accuracy: 0.9730, Val Loss: 0.0859, Val Accuracy: 0.9697
Epoch 434/1000, Train Loss: 0.0845, Train Accuracy: 0.9748, Val Loss: 0.0856, Val Accuracy: 0.9699
Epoch 435/1000, Train Loss: 0.0866, Train Accuracy: 0.9736, Val Loss: 0.0855, Val Accuracy: 0.9699
Epoch 436/1000, Train Loss: 0.0867, Train Accuracy: 0.9736, Val Loss: 0.0852, Val Accuracy: 0.9699
Epoch 437/1000, Train Loss: 0.0849, Train Accuracy: 0.9746, Val Loss: 0.0851, Val Accuracy: 0.9699
Epoch 438/1000, Train Loss: 0.0859, Train Accuracy: 0.9741, Val Loss: 0.0850, Val Accuracy: 0.9705
Epoch 439/1000, Train Loss: 0.0861, Train Accuracy: 0.9729, Val Loss: 0.0848, Val Accuracy: 0.9705
Epoch 440/1000, Train Loss: 0.0839, Train Accuracy: 0.9743, Val Loss: 0.0845, Val Accuracy: 0.9705
Epoch 441/1000, Train Loss: 0.0866, Train Accuracy: 0.9728, Val Loss: 0.0845, Val Accuracy: 0.9708
Epoch 442/1000, Train Loss: 0.0851, Train Accuracy: 0.9743, Val Loss: 0.0843, Val Accuracy: 0.9708
Epoch 443/1000, Train Loss: 0.0848, Train Accuracy: 0.9746, Val Loss: 0.0842, Val Accuracy: 0.9708
Epoch 444/1000, Train Loss: 0.0833, Train Accuracy: 0.9747, Val Loss: 0.0840, Val Accuracy: 0.9714
Epoch 445/1000, Train Loss: 0.0840, Train Accuracy: 0.9736, Val Loss: 0.0838, Val Accuracy: 0.9711
Epoch 446/1000, Train Loss: 0.0843, Train Accuracy: 0.9738, Val Loss: 0.0837, Val Accuracy: 0.9714
Epoch 447/1000, Train Loss: 0.0833, Train Accuracy: 0.9743, Val Loss: 0.0835, Val Accuracy: 0.9714
Epoch 448/1000, Train Loss: 0.0848, Train Accuracy: 0.9734, Val Loss: 0.0834, Val Accuracy: 0.9711
Epoch 449/1000, Train Loss: 0.0848, Train Accuracy: 0.9734, Val Loss: 0.0833, Val Accuracy: 0.9708
Epoch 450/1000, Train Loss: 0.0856, Train Accuracy: 0.9725, Val Loss: 0.0832, Val Accuracy: 0.9714
Epoch 451/1000, Train Loss: 0.0827, Train Accuracy: 0.9741, Val Loss: 0.0830, Val Accuracy: 0.9711
Epoch 452/1000, Train Loss: 0.0840, Train Accuracy: 0.9729, Val Loss: 0.0830, Val Accuracy: 0.9714
Epoch 453/1000, Train Loss: 0.0818, Train Accuracy: 0.9752, Val Loss: 0.0828, Val Accuracy: 0.9714
Epoch 454/1000, Train Loss: 0.0826, Train Accuracy: 0.9733, Val Loss: 0.0827, Val Accuracy: 0.9714
Epoch 455/1000, Train Loss: 0.0821, Train Accuracy: 0.9738, Val Loss: 0.0824, Val Accuracy: 0.9711
Epoch 456/1000, Train Loss: 0.0822, Train Accuracy: 0.9743, Val Loss: 0.0823, Val Accuracy: 0.9714
Epoch 457/1000, Train Loss: 0.0822, Train Accuracy: 0.9735, Val Loss: 0.0820, Val Accuracy: 0.9714
Epoch 458/1000, Train Loss: 0.0831, Train Accuracy: 0.9746, Val Loss: 0.0818, Val Accuracy: 0.9714
Epoch 459/1000, Train Loss: 0.0809, Train Accuracy: 0.9736, Val Loss: 0.0817, Val Accuracy: 0.9714
Epoch 460/1000, Train Loss: 0.0817, Train Accuracy: 0.9743, Val Loss: 0.0815, Val Accuracy: 0.9714
Epoch 461/1000, Train Loss: 0.0808, Train Accuracy: 0.9741, Val Loss: 0.0815, Val Accuracy: 0.9714
Epoch 462/1000, Train Loss: 0.0800, Train Accuracy: 0.9746, Val Loss: 0.0812, Val Accuracy: 0.9714
Epoch 463/1000, Train Loss: 0.0802, Train Accuracy: 0.9738, Val Loss: 0.0811, Val Accuracy: 0.9714
Epoch 464/1000, Train Loss: 0.0815, Train Accuracy: 0.9746, Val Loss: 0.0809, Val Accuracy: 0.9711
Epoch 465/1000, Train Loss: 0.0814, Train Accuracy: 0.9741, Val Loss: 0.0809, Val Accuracy: 0.9711
Epoch 466/1000, Train Loss: 0.0820, Train Accuracy: 0.9738, Val Loss: 0.0809, Val Accuracy: 0.9714
Epoch 467/1000, Train Loss: 0.0797, Train Accuracy: 0.9764, Val Loss: 0.0807, Val Accuracy: 0.9714
Epoch 468/1000, Train Loss: 0.0812, Train Accuracy: 0.9740, Val Loss: 0.0805, Val Accuracy: 0.9711
Epoch 469/1000, Train Loss: 0.0803, Train Accuracy: 0.9748, Val Loss: 0.0804, Val Accuracy: 0.9711
Epoch 470/1000, Train Loss: 0.0807, Train Accuracy: 0.9748, Val Loss: 0.0802, Val Accuracy: 0.9711
Epoch 471/1000, Train Loss: 0.0781, Train Accuracy: 0.9760, Val Loss: 0.0801, Val Accuracy: 0.9714
Epoch 472/1000, Train Loss: 0.0795, Train Accuracy: 0.9755, Val Loss: 0.0799, Val Accuracy: 0.9714
Epoch 473/1000, Train Loss: 0.0789, Train Accuracy: 0.9769, Val Loss: 0.0798, Val Accuracy: 0.9714
Epoch 474/1000, Train Loss: 0.0778, Train Accuracy: 0.9751, Val Loss: 0.0796, Val Accuracy: 0.9714
Epoch 475/1000, Train Loss: 0.0796, Train Accuracy: 0.9750, Val Loss: 0.0794, Val Accuracy: 0.9714
Epoch 476/1000, Train Loss: 0.0782, Train Accuracy: 0.9746, Val Loss: 0.0793, Val Accuracy: 0.9717
Epoch 477/1000, Train Loss: 0.0788, Train Accuracy: 0.9754, Val Loss: 0.0792, Val Accuracy: 0.9717
Epoch 478/1000, Train Loss: 0.0783, Train Accuracy: 0.9759, Val Loss: 0.0792, Val Accuracy: 0.9717
Epoch 479/1000, Train Loss: 0.0790, Train Accuracy: 0.9748, Val Loss: 0.0790, Val Accuracy: 0.9717
Epoch 480/1000, Train Loss: 0.0774, Train Accuracy: 0.9762, Val Loss: 0.0789, Val Accuracy: 0.9717
Epoch 481/1000, Train Loss: 0.0775, Train Accuracy: 0.9754, Val Loss: 0.0788, Val Accuracy: 0.9717
Epoch 482/1000, Train Loss: 0.0781, Train Accuracy: 0.9766, Val Loss: 0.0786, Val Accuracy: 0.9717
Epoch 483/1000, Train Loss: 0.0766, Train Accuracy: 0.9767, Val Loss: 0.0785, Val Accuracy: 0.9717
Epoch 484/1000, Train Loss: 0.0765, Train Accuracy: 0.9759, Val Loss: 0.0784, Val Accuracy: 0.9717
Epoch 485/1000, Train Loss: 0.0778, Train Accuracy: 0.9751, Val Loss: 0.0783, Val Accuracy: 0.9717
Epoch 486/1000, Train Loss: 0.0775, Train Accuracy: 0.9757, Val Loss: 0.0782, Val Accuracy: 0.9717
Epoch 487/1000, Train Loss: 0.0767, Train Accuracy: 0.9764, Val Loss: 0.0781, Val Accuracy: 0.9717
Epoch 488/1000, Train Loss: 0.0774, Train Accuracy: 0.9759, Val Loss: 0.0779, Val Accuracy: 0.9717
Epoch 489/1000, Train Loss: 0.0761, Train Accuracy: 0.9766, Val Loss: 0.0778, Val Accuracy: 0.9717
Epoch 490/1000, Train Loss: 0.0786, Train Accuracy: 0.9743, Val Loss: 0.0778, Val Accuracy: 0.9717
Epoch 491/1000, Train Loss: 0.0771, Train Accuracy: 0.9772, Val Loss: 0.0776, Val Accuracy: 0.9717
Epoch 492/1000, Train Loss: 0.0767, Train Accuracy: 0.9757, Val Loss: 0.0775, Val Accuracy: 0.9720
Epoch 493/1000, Train Loss: 0.0760, Train Accuracy: 0.9756, Val Loss: 0.0773, Val Accuracy: 0.9717
Epoch 494/1000, Train Loss: 0.0772, Train Accuracy: 0.9757, Val Loss: 0.0773, Val Accuracy: 0.9717
Epoch 495/1000, Train Loss: 0.0769, Train Accuracy: 0.9755, Val Loss: 0.0771, Val Accuracy: 0.9717
Epoch 496/1000, Train Loss: 0.0767, Train Accuracy: 0.9755, Val Loss: 0.0769, Val Accuracy: 0.9717
Epoch 497/1000, Train Loss: 0.0738, Train Accuracy: 0.9771, Val Loss: 0.0769, Val Accuracy: 0.9717
Epoch 498/1000, Train Loss: 0.0742, Train Accuracy: 0.9772, Val Loss: 0.0768, Val Accuracy: 0.9717
Epoch 499/1000, Train Loss: 0.0748, Train Accuracy: 0.9759, Val Loss: 0.0766, Val Accuracy: 0.9717
Epoch 500/1000, Train Loss: 0.0764, Train Accuracy: 0.9759, Val Loss: 0.0765, Val Accuracy: 0.9717
Epoch 501/1000, Train Loss: 0.0753, Train Accuracy: 0.9769, Val Loss: 0.0764, Val Accuracy: 0.9717
Epoch 502/1000, Train Loss: 0.0755, Train Accuracy: 0.9759, Val Loss: 0.0763, Val Accuracy: 0.9717
Epoch 503/1000, Train Loss: 0.0750, Train Accuracy: 0.9764, Val Loss: 0.0762, Val Accuracy: 0.9717
Epoch 504/1000, Train Loss: 0.0726, Train Accuracy: 0.9773, Val Loss: 0.0761, Val Accuracy: 0.9717
Epoch 505/1000, Train Loss: 0.0742, Train Accuracy: 0.9763, Val Loss: 0.0759, Val Accuracy: 0.9717
Epoch 506/1000, Train Loss: 0.0738, Train Accuracy: 0.9766, Val Loss: 0.0756, Val Accuracy: 0.9720
Epoch 507/1000, Train Loss: 0.0753, Train Accuracy: 0.9762, Val Loss: 0.0757, Val Accuracy: 0.9717
Epoch 508/1000, Train Loss: 0.0752, Train Accuracy: 0.9771, Val Loss: 0.0758, Val Accuracy: 0.9717
Epoch 509/1000, Train Loss: 0.0743, Train Accuracy: 0.9768, Val Loss: 0.0757, Val Accuracy: 0.9717
Epoch 510/1000, Train Loss: 0.0741, Train Accuracy: 0.9759, Val Loss: 0.0755, Val Accuracy: 0.9717
Epoch 511/1000, Train Loss: 0.0724, Train Accuracy: 0.9766, Val Loss: 0.0753, Val Accuracy: 0.9725
Epoch 512/1000, Train Loss: 0.0723, Train Accuracy: 0.9783, Val Loss: 0.0753, Val Accuracy: 0.9725
Epoch 513/1000, Train Loss: 0.0749, Train Accuracy: 0.9767, Val Loss: 0.0752, Val Accuracy: 0.9725
Epoch 514/1000, Train Loss: 0.0737, Train Accuracy: 0.9760, Val Loss: 0.0752, Val Accuracy: 0.9723
Epoch 515/1000, Train Loss: 0.0734, Train Accuracy: 0.9762, Val Loss: 0.0750, Val Accuracy: 0.9725
Epoch 516/1000, Train Loss: 0.0712, Train Accuracy: 0.9776, Val Loss: 0.0750, Val Accuracy: 0.9725
Epoch 517/1000, Train Loss: 0.0718, Train Accuracy: 0.9774, Val Loss: 0.0749, Val Accuracy: 0.9720
Epoch 518/1000, Train Loss: 0.0725, Train Accuracy: 0.9773, Val Loss: 0.0747, Val Accuracy: 0.9725
Epoch 519/1000, Train Loss: 0.0719, Train Accuracy: 0.9754, Val Loss: 0.0745, Val Accuracy: 0.9725
Epoch 520/1000, Train Loss: 0.0724, Train Accuracy: 0.9781, Val Loss: 0.0743, Val Accuracy: 0.9725
Epoch 521/1000, Train Loss: 0.0738, Train Accuracy: 0.9764, Val Loss: 0.0743, Val Accuracy: 0.9728
Epoch 522/1000, Train Loss: 0.0725, Train Accuracy: 0.9762, Val Loss: 0.0742, Val Accuracy: 0.9728
Epoch 523/1000, Train Loss: 0.0720, Train Accuracy: 0.9770, Val Loss: 0.0741, Val Accuracy: 0.9725
Epoch 524/1000, Train Loss: 0.0720, Train Accuracy: 0.9779, Val Loss: 0.0740, Val Accuracy: 0.9725
Epoch 525/1000, Train Loss: 0.0719, Train Accuracy: 0.9783, Val Loss: 0.0739, Val Accuracy: 0.9728
Epoch 526/1000, Train Loss: 0.0710, Train Accuracy: 0.9778, Val Loss: 0.0739, Val Accuracy: 0.9725
Epoch 527/1000, Train Loss: 0.0714, Train Accuracy: 0.9784, Val Loss: 0.0738, Val Accuracy: 0.9725
Epoch 528/1000, Train Loss: 0.0728, Train Accuracy: 0.9759, Val Loss: 0.0735, Val Accuracy: 0.9728
Epoch 529/1000, Train Loss: 0.0717, Train Accuracy: 0.9768, Val Loss: 0.0735, Val Accuracy: 0.9725
Epoch 530/1000, Train Loss: 0.0706, Train Accuracy: 0.9775, Val Loss: 0.0736, Val Accuracy: 0.9728
Epoch 531/1000, Train Loss: 0.0706, Train Accuracy: 0.9785, Val Loss: 0.0734, Val Accuracy: 0.9728
Epoch 532/1000, Train Loss: 0.0711, Train Accuracy: 0.9775, Val Loss: 0.0735, Val Accuracy: 0.9728
Epoch 533/1000, Train Loss: 0.0697, Train Accuracy: 0.9773, Val Loss: 0.0734, Val Accuracy: 0.9728
Epoch 534/1000, Train Loss: 0.0713, Train Accuracy: 0.9769, Val Loss: 0.0732, Val Accuracy: 0.9728
Epoch 535/1000, Train Loss: 0.0715, Train Accuracy: 0.9774, Val Loss: 0.0730, Val Accuracy: 0.9731
Epoch 536/1000, Train Loss: 0.0718, Train Accuracy: 0.9771, Val Loss: 0.0729, Val Accuracy: 0.9731
Epoch 537/1000, Train Loss: 0.0724, Train Accuracy: 0.9767, Val Loss: 0.0727, Val Accuracy: 0.9731
Epoch 538/1000, Train Loss: 0.0687, Train Accuracy: 0.9785, Val Loss: 0.0727, Val Accuracy: 0.9731
Epoch 539/1000, Train Loss: 0.0702, Train Accuracy: 0.9771, Val Loss: 0.0727, Val Accuracy: 0.9728
Epoch 540/1000, Train Loss: 0.0700, Train Accuracy: 0.9781, Val Loss: 0.0727, Val Accuracy: 0.9725
Epoch 541/1000, Train Loss: 0.0708, Train Accuracy: 0.9771, Val Loss: 0.0726, Val Accuracy: 0.9725
Epoch 542/1000, Train Loss: 0.0690, Train Accuracy: 0.9788, Val Loss: 0.0726, Val Accuracy: 0.9725
Epoch 543/1000, Train Loss: 0.0705, Train Accuracy: 0.9779, Val Loss: 0.0724, Val Accuracy: 0.9728
Epoch 544/1000, Train Loss: 0.0709, Train Accuracy: 0.9774, Val Loss: 0.0723, Val Accuracy: 0.9728
Epoch 545/1000, Train Loss: 0.0678, Train Accuracy: 0.9777, Val Loss: 0.0722, Val Accuracy: 0.9728
Epoch 546/1000, Train Loss: 0.0681, Train Accuracy: 0.9782, Val Loss: 0.0721, Val Accuracy: 0.9734
Epoch 547/1000, Train Loss: 0.0693, Train Accuracy: 0.9788, Val Loss: 0.0719, Val Accuracy: 0.9737
Epoch 548/1000, Train Loss: 0.0697, Train Accuracy: 0.9775, Val Loss: 0.0718, Val Accuracy: 0.9737
Epoch 549/1000, Train Loss: 0.0690, Train Accuracy: 0.9765, Val Loss: 0.0717, Val Accuracy: 0.9737
Epoch 550/1000, Train Loss: 0.0688, Train Accuracy: 0.9777, Val Loss: 0.0717, Val Accuracy: 0.9737
Epoch 551/1000, Train Loss: 0.0678, Train Accuracy: 0.9778, Val Loss: 0.0716, Val Accuracy: 0.9737
Epoch 552/1000, Train Loss: 0.0697, Train Accuracy: 0.9775, Val Loss: 0.0716, Val Accuracy: 0.9734
Epoch 553/1000, Train Loss: 0.0676, Train Accuracy: 0.9788, Val Loss: 0.0716, Val Accuracy: 0.9731
Epoch 554/1000, Train Loss: 0.0703, Train Accuracy: 0.9773, Val Loss: 0.0716, Val Accuracy: 0.9728
Epoch 555/1000, Train Loss: 0.0687, Train Accuracy: 0.9780, Val Loss: 0.0715, Val Accuracy: 0.9731
Epoch 556/1000, Train Loss: 0.0680, Train Accuracy: 0.9782, Val Loss: 0.0713, Val Accuracy: 0.9734
Epoch 557/1000, Train Loss: 0.0687, Train Accuracy: 0.9779, Val Loss: 0.0713, Val Accuracy: 0.9734
Epoch 558/1000, Train Loss: 0.0699, Train Accuracy: 0.9790, Val Loss: 0.0714, Val Accuracy: 0.9731
Epoch 559/1000, Train Loss: 0.0668, Train Accuracy: 0.9781, Val Loss: 0.0713, Val Accuracy: 0.9734
Epoch 560/1000, Train Loss: 0.0687, Train Accuracy: 0.9772, Val Loss: 0.0711, Val Accuracy: 0.9734
Epoch 561/1000, Train Loss: 0.0673, Train Accuracy: 0.9779, Val Loss: 0.0711, Val Accuracy: 0.9731
Epoch 562/1000, Train Loss: 0.0662, Train Accuracy: 0.9784, Val Loss: 0.0710, Val Accuracy: 0.9734
Epoch 563/1000, Train Loss: 0.0681, Train Accuracy: 0.9778, Val Loss: 0.0709, Val Accuracy: 0.9734
Epoch 564/1000, Train Loss: 0.0673, Train Accuracy: 0.9780, Val Loss: 0.0708, Val Accuracy: 0.9734
Epoch 565/1000, Train Loss: 0.0695, Train Accuracy: 0.9766, Val Loss: 0.0706, Val Accuracy: 0.9734
Epoch 566/1000, Train Loss: 0.0671, Train Accuracy: 0.9779, Val Loss: 0.0706, Val Accuracy: 0.9731
Epoch 567/1000, Train Loss: 0.0656, Train Accuracy: 0.9785, Val Loss: 0.0705, Val Accuracy: 0.9734
Epoch 568/1000, Train Loss: 0.0678, Train Accuracy: 0.9778, Val Loss: 0.0705, Val Accuracy: 0.9731
Epoch 569/1000, Train Loss: 0.0661, Train Accuracy: 0.9793, Val Loss: 0.0703, Val Accuracy: 0.9728
Epoch 570/1000, Train Loss: 0.0672, Train Accuracy: 0.9782, Val Loss: 0.0703, Val Accuracy: 0.9728
Epoch 571/1000, Train Loss: 0.0664, Train Accuracy: 0.9785, Val Loss: 0.0703, Val Accuracy: 0.9728
Epoch 572/1000, Train Loss: 0.0671, Train Accuracy: 0.9790, Val Loss: 0.0702, Val Accuracy: 0.9728
Epoch 573/1000, Train Loss: 0.0660, Train Accuracy: 0.9793, Val Loss: 0.0701, Val Accuracy: 0.9728
Epoch 574/1000, Train Loss: 0.0657, Train Accuracy: 0.9780, Val Loss: 0.0700, Val Accuracy: 0.9728
Epoch 575/1000, Train Loss: 0.0659, Train Accuracy: 0.9793, Val Loss: 0.0699, Val Accuracy: 0.9731
Epoch 576/1000, Train Loss: 0.0672, Train Accuracy: 0.9788, Val Loss: 0.0698, Val Accuracy: 0.9734
Epoch 577/1000, Train Loss: 0.0647, Train Accuracy: 0.9799, Val Loss: 0.0700, Val Accuracy: 0.9728
Epoch 578/1000, Train Loss: 0.0666, Train Accuracy: 0.9795, Val Loss: 0.0699, Val Accuracy: 0.9728
Epoch 579/1000, Train Loss: 0.0677, Train Accuracy: 0.9794, Val Loss: 0.0698, Val Accuracy: 0.9728
Epoch 580/1000, Train Loss: 0.0661, Train Accuracy: 0.9788, Val Loss: 0.0698, Val Accuracy: 0.9731
Epoch 581/1000, Train Loss: 0.0657, Train Accuracy: 0.9788, Val Loss: 0.0698, Val Accuracy: 0.9728
Epoch 582/1000, Train Loss: 0.0668, Train Accuracy: 0.9788, Val Loss: 0.0697, Val Accuracy: 0.9728
Epoch 583/1000, Train Loss: 0.0662, Train Accuracy: 0.9791, Val Loss: 0.0697, Val Accuracy: 0.9725
Epoch 584/1000, Train Loss: 0.0654, Train Accuracy: 0.9793, Val Loss: 0.0696, Val Accuracy: 0.9728
Epoch 585/1000, Train Loss: 0.0676, Train Accuracy: 0.9786, Val Loss: 0.0694, Val Accuracy: 0.9731
Epoch 586/1000, Train Loss: 0.0655, Train Accuracy: 0.9790, Val Loss: 0.0693, Val Accuracy: 0.9731
Epoch 587/1000, Train Loss: 0.0640, Train Accuracy: 0.9803, Val Loss: 0.0692, Val Accuracy: 0.9728
Epoch 588/1000, Train Loss: 0.0649, Train Accuracy: 0.9795, Val Loss: 0.0690, Val Accuracy: 0.9734
Epoch 589/1000, Train Loss: 0.0642, Train Accuracy: 0.9806, Val Loss: 0.0689, Val Accuracy: 0.9734
Epoch 590/1000, Train Loss: 0.0657, Train Accuracy: 0.9790, Val Loss: 0.0691, Val Accuracy: 0.9728
Epoch 591/1000, Train Loss: 0.0630, Train Accuracy: 0.9805, Val Loss: 0.0690, Val Accuracy: 0.9731
Epoch 592/1000, Train Loss: 0.0652, Train Accuracy: 0.9801, Val Loss: 0.0690, Val Accuracy: 0.9728
Epoch 593/1000, Train Loss: 0.0657, Train Accuracy: 0.9792, Val Loss: 0.0690, Val Accuracy: 0.9728
Epoch 594/1000, Train Loss: 0.0649, Train Accuracy: 0.9789, Val Loss: 0.0687, Val Accuracy: 0.9734
Epoch 595/1000, Train Loss: 0.0663, Train Accuracy: 0.9793, Val Loss: 0.0687, Val Accuracy: 0.9737
Epoch 596/1000, Train Loss: 0.0658, Train Accuracy: 0.9785, Val Loss: 0.0685, Val Accuracy: 0.9734
Epoch 597/1000, Train Loss: 0.0640, Train Accuracy: 0.9801, Val Loss: 0.0684, Val Accuracy: 0.9734
Epoch 598/1000, Train Loss: 0.0627, Train Accuracy: 0.9790, Val Loss: 0.0685, Val Accuracy: 0.9734
Epoch 599/1000, Train Loss: 0.0652, Train Accuracy: 0.9789, Val Loss: 0.0683, Val Accuracy: 0.9734
Epoch 600/1000, Train Loss: 0.0632, Train Accuracy: 0.9793, Val Loss: 0.0684, Val Accuracy: 0.9734
Epoch 601/1000, Train Loss: 0.0629, Train Accuracy: 0.9791, Val Loss: 0.0682, Val Accuracy: 0.9734
Epoch 602/1000, Train Loss: 0.0644, Train Accuracy: 0.9800, Val Loss: 0.0682, Val Accuracy: 0.9734
Epoch 603/1000, Train Loss: 0.0637, Train Accuracy: 0.9789, Val Loss: 0.0681, Val Accuracy: 0.9734
Epoch 604/1000, Train Loss: 0.0637, Train Accuracy: 0.9792, Val Loss: 0.0682, Val Accuracy: 0.9731
Epoch 605/1000, Train Loss: 0.0643, Train Accuracy: 0.9793, Val Loss: 0.0680, Val Accuracy: 0.9734
Epoch 606/1000, Train Loss: 0.0644, Train Accuracy: 0.9798, Val Loss: 0.0680, Val Accuracy: 0.9731
Epoch 607/1000, Train Loss: 0.0617, Train Accuracy: 0.9797, Val Loss: 0.0681, Val Accuracy: 0.9728
Epoch 608/1000, Train Loss: 0.0633, Train Accuracy: 0.9792, Val Loss: 0.0678, Val Accuracy: 0.9734
Epoch 609/1000, Train Loss: 0.0617, Train Accuracy: 0.9796, Val Loss: 0.0678, Val Accuracy: 0.9734
Epoch 610/1000, Train Loss: 0.0642, Train Accuracy: 0.9799, Val Loss: 0.0677, Val Accuracy: 0.9734
Epoch 611/1000, Train Loss: 0.0651, Train Accuracy: 0.9790, Val Loss: 0.0677, Val Accuracy: 0.9734
Epoch 612/1000, Train Loss: 0.0610, Train Accuracy: 0.9807, Val Loss: 0.0676, Val Accuracy: 0.9731
Epoch 613/1000, Train Loss: 0.0630, Train Accuracy: 0.9782, Val Loss: 0.0676, Val Accuracy: 0.9731
Epoch 614/1000, Train Loss: 0.0630, Train Accuracy: 0.9793, Val Loss: 0.0674, Val Accuracy: 0.9734
Epoch 615/1000, Train Loss: 0.0633, Train Accuracy: 0.9787, Val Loss: 0.0676, Val Accuracy: 0.9734
Epoch 616/1000, Train Loss: 0.0629, Train Accuracy: 0.9803, Val Loss: 0.0675, Val Accuracy: 0.9737
Epoch 617/1000, Train Loss: 0.0638, Train Accuracy: 0.9792, Val Loss: 0.0676, Val Accuracy: 0.9737
Epoch 618/1000, Train Loss: 0.0629, Train Accuracy: 0.9801, Val Loss: 0.0676, Val Accuracy: 0.9737
Epoch 619/1000, Train Loss: 0.0607, Train Accuracy: 0.9806, Val Loss: 0.0675, Val Accuracy: 0.9737
Epoch 620/1000, Train Loss: 0.0623, Train Accuracy: 0.9802, Val Loss: 0.0674, Val Accuracy: 0.9737
Epoch 621/1000, Train Loss: 0.0610, Train Accuracy: 0.9802, Val Loss: 0.0673, Val Accuracy: 0.9740
Epoch 622/1000, Train Loss: 0.0613, Train Accuracy: 0.9811, Val Loss: 0.0673, Val Accuracy: 0.9740
Epoch 623/1000, Train Loss: 0.0635, Train Accuracy: 0.9786, Val Loss: 0.0671, Val Accuracy: 0.9743
Epoch 624/1000, Train Loss: 0.0598, Train Accuracy: 0.9814, Val Loss: 0.0669, Val Accuracy: 0.9743
Epoch 625/1000, Train Loss: 0.0607, Train Accuracy: 0.9809, Val Loss: 0.0670, Val Accuracy: 0.9737
Epoch 626/1000, Train Loss: 0.0612, Train Accuracy: 0.9801, Val Loss: 0.0669, Val Accuracy: 0.9737
Epoch 627/1000, Train Loss: 0.0610, Train Accuracy: 0.9799, Val Loss: 0.0669, Val Accuracy: 0.9740
Epoch 628/1000, Train Loss: 0.0611, Train Accuracy: 0.9803, Val Loss: 0.0667, Val Accuracy: 0.9740
Epoch 629/1000, Train Loss: 0.0606, Train Accuracy: 0.9801, Val Loss: 0.0669, Val Accuracy: 0.9743
Epoch 630/1000, Train Loss: 0.0614, Train Accuracy: 0.9798, Val Loss: 0.0667, Val Accuracy: 0.9743
Epoch 631/1000, Train Loss: 0.0614, Train Accuracy: 0.9796, Val Loss: 0.0667, Val Accuracy: 0.9746
Epoch 632/1000, Train Loss: 0.0612, Train Accuracy: 0.9803, Val Loss: 0.0668, Val Accuracy: 0.9746
Epoch 633/1000, Train Loss: 0.0609, Train Accuracy: 0.9806, Val Loss: 0.0667, Val Accuracy: 0.9749
Epoch 634/1000, Train Loss: 0.0613, Train Accuracy: 0.9807, Val Loss: 0.0666, Val Accuracy: 0.9749
Epoch 635/1000, Train Loss: 0.0611, Train Accuracy: 0.9802, Val Loss: 0.0665, Val Accuracy: 0.9749
Epoch 636/1000, Train Loss: 0.0620, Train Accuracy: 0.9795, Val Loss: 0.0664, Val Accuracy: 0.9749
Epoch 637/1000, Train Loss: 0.0604, Train Accuracy: 0.9803, Val Loss: 0.0665, Val Accuracy: 0.9749
Epoch 638/1000, Train Loss: 0.0619, Train Accuracy: 0.9784, Val Loss: 0.0663, Val Accuracy: 0.9749
Epoch 639/1000, Train Loss: 0.0596, Train Accuracy: 0.9808, Val Loss: 0.0662, Val Accuracy: 0.9749
Epoch 640/1000, Train Loss: 0.0595, Train Accuracy: 0.9811, Val Loss: 0.0662, Val Accuracy: 0.9749
Epoch 641/1000, Train Loss: 0.0620, Train Accuracy: 0.9787, Val Loss: 0.0663, Val Accuracy: 0.9749
Epoch 642/1000, Train Loss: 0.0602, Train Accuracy: 0.9817, Val Loss: 0.0662, Val Accuracy: 0.9749
Epoch 643/1000, Train Loss: 0.0605, Train Accuracy: 0.9799, Val Loss: 0.0662, Val Accuracy: 0.9749
Epoch 644/1000, Train Loss: 0.0596, Train Accuracy: 0.9814, Val Loss: 0.0660, Val Accuracy: 0.9749
Epoch 645/1000, Train Loss: 0.0597, Train Accuracy: 0.9801, Val Loss: 0.0662, Val Accuracy: 0.9751
Epoch 646/1000, Train Loss: 0.0605, Train Accuracy: 0.9809, Val Loss: 0.0662, Val Accuracy: 0.9751
Epoch 647/1000, Train Loss: 0.0607, Train Accuracy: 0.9803, Val Loss: 0.0661, Val Accuracy: 0.9751
Epoch 648/1000, Train Loss: 0.0611, Train Accuracy: 0.9800, Val Loss: 0.0659, Val Accuracy: 0.9749
Epoch 649/1000, Train Loss: 0.0615, Train Accuracy: 0.9801, Val Loss: 0.0659, Val Accuracy: 0.9749
Epoch 650/1000, Train Loss: 0.0587, Train Accuracy: 0.9819, Val Loss: 0.0659, Val Accuracy: 0.9751
Epoch 651/1000, Train Loss: 0.0602, Train Accuracy: 0.9813, Val Loss: 0.0658, Val Accuracy: 0.9751
Epoch 652/1000, Train Loss: 0.0605, Train Accuracy: 0.9801, Val Loss: 0.0658, Val Accuracy: 0.9751
Epoch 653/1000, Train Loss: 0.0592, Train Accuracy: 0.9812, Val Loss: 0.0657, Val Accuracy: 0.9751
Epoch 654/1000, Train Loss: 0.0580, Train Accuracy: 0.9822, Val Loss: 0.0659, Val Accuracy: 0.9754
Epoch 655/1000, Train Loss: 0.0573, Train Accuracy: 0.9808, Val Loss: 0.0658, Val Accuracy: 0.9754
Epoch 656/1000, Train Loss: 0.0593, Train Accuracy: 0.9810, Val Loss: 0.0655, Val Accuracy: 0.9751
Epoch 657/1000, Train Loss: 0.0610, Train Accuracy: 0.9800, Val Loss: 0.0654, Val Accuracy: 0.9751
Epoch 658/1000, Train Loss: 0.0590, Train Accuracy: 0.9806, Val Loss: 0.0654, Val Accuracy: 0.9751
Epoch 659/1000, Train Loss: 0.0566, Train Accuracy: 0.9824, Val Loss: 0.0654, Val Accuracy: 0.9751
Epoch 660/1000, Train Loss: 0.0597, Train Accuracy: 0.9817, Val Loss: 0.0656, Val Accuracy: 0.9754
Epoch 661/1000, Train Loss: 0.0601, Train Accuracy: 0.9800, Val Loss: 0.0653, Val Accuracy: 0.9751
Epoch 662/1000, Train Loss: 0.0583, Train Accuracy: 0.9803, Val Loss: 0.0655, Val Accuracy: 0.9754
Epoch 663/1000, Train Loss: 0.0586, Train Accuracy: 0.9813, Val Loss: 0.0654, Val Accuracy: 0.9754
Epoch 664/1000, Train Loss: 0.0586, Train Accuracy: 0.9813, Val Loss: 0.0654, Val Accuracy: 0.9754
Epoch 665/1000, Train Loss: 0.0583, Train Accuracy: 0.9811, Val Loss: 0.0652, Val Accuracy: 0.9754
Epoch 666/1000, Train Loss: 0.0579, Train Accuracy: 0.9808, Val Loss: 0.0653, Val Accuracy: 0.9754
Epoch 667/1000, Train Loss: 0.0592, Train Accuracy: 0.9806, Val Loss: 0.0652, Val Accuracy: 0.9754
Epoch 668/1000, Train Loss: 0.0597, Train Accuracy: 0.9801, Val Loss: 0.0654, Val Accuracy: 0.9754
Epoch 669/1000, Train Loss: 0.0581, Train Accuracy: 0.9806, Val Loss: 0.0653, Val Accuracy: 0.9754
Epoch 670/1000, Train Loss: 0.0567, Train Accuracy: 0.9813, Val Loss: 0.0653, Val Accuracy: 0.9757
Epoch 671/1000, Train Loss: 0.0566, Train Accuracy: 0.9814, Val Loss: 0.0652, Val Accuracy: 0.9754
Epoch 672/1000, Train Loss: 0.0578, Train Accuracy: 0.9811, Val Loss: 0.0653, Val Accuracy: 0.9754
Epoch 673/1000, Train Loss: 0.0568, Train Accuracy: 0.9816, Val Loss: 0.0651, Val Accuracy: 0.9754
Epoch 674/1000, Train Loss: 0.0570, Train Accuracy: 0.9820, Val Loss: 0.0649, Val Accuracy: 0.9757
Epoch 675/1000, Train Loss: 0.0570, Train Accuracy: 0.9816, Val Loss: 0.0650, Val Accuracy: 0.9754
Epoch 676/1000, Train Loss: 0.0558, Train Accuracy: 0.9824, Val Loss: 0.0649, Val Accuracy: 0.9754
Epoch 677/1000, Train Loss: 0.0595, Train Accuracy: 0.9805, Val Loss: 0.0647, Val Accuracy: 0.9754
Epoch 678/1000, Train Loss: 0.0557, Train Accuracy: 0.9825, Val Loss: 0.0648, Val Accuracy: 0.9754
Epoch 679/1000, Train Loss: 0.0556, Train Accuracy: 0.9823, Val Loss: 0.0648, Val Accuracy: 0.9754
Epoch 680/1000, Train Loss: 0.0572, Train Accuracy: 0.9813, Val Loss: 0.0649, Val Accuracy: 0.9760
Epoch 681/1000, Train Loss: 0.0557, Train Accuracy: 0.9810, Val Loss: 0.0650, Val Accuracy: 0.9757
Epoch 682/1000, Train Loss: 0.0547, Train Accuracy: 0.9817, Val Loss: 0.0648, Val Accuracy: 0.9760
Epoch 683/1000, Train Loss: 0.0556, Train Accuracy: 0.9825, Val Loss: 0.0644, Val Accuracy: 0.9760
Epoch 684/1000, Train Loss: 0.0597, Train Accuracy: 0.9798, Val Loss: 0.0645, Val Accuracy: 0.9763
Epoch 685/1000, Train Loss: 0.0570, Train Accuracy: 0.9811, Val Loss: 0.0646, Val Accuracy: 0.9763
Epoch 686/1000, Train Loss: 0.0568, Train Accuracy: 0.9807, Val Loss: 0.0644, Val Accuracy: 0.9763
Epoch 687/1000, Train Loss: 0.0564, Train Accuracy: 0.9818, Val Loss: 0.0643, Val Accuracy: 0.9763
Epoch 688/1000, Train Loss: 0.0573, Train Accuracy: 0.9816, Val Loss: 0.0643, Val Accuracy: 0.9763
Epoch 689/1000, Train Loss: 0.0531, Train Accuracy: 0.9834, Val Loss: 0.0643, Val Accuracy: 0.9763
Epoch 690/1000, Train Loss: 0.0549, Train Accuracy: 0.9819, Val Loss: 0.0644, Val Accuracy: 0.9763
Epoch 691/1000, Train Loss: 0.0562, Train Accuracy: 0.9817, Val Loss: 0.0642, Val Accuracy: 0.9763
Epoch 692/1000, Train Loss: 0.0546, Train Accuracy: 0.9824, Val Loss: 0.0643, Val Accuracy: 0.9763
Epoch 693/1000, Train Loss: 0.0564, Train Accuracy: 0.9816, Val Loss: 0.0641, Val Accuracy: 0.9763
Epoch 694/1000, Train Loss: 0.0545, Train Accuracy: 0.9829, Val Loss: 0.0640, Val Accuracy: 0.9760
Epoch 695/1000, Train Loss: 0.0571, Train Accuracy: 0.9809, Val Loss: 0.0641, Val Accuracy: 0.9760
Epoch 696/1000, Train Loss: 0.0572, Train Accuracy: 0.9809, Val Loss: 0.0641, Val Accuracy: 0.9760
Epoch 697/1000, Train Loss: 0.0553, Train Accuracy: 0.9814, Val Loss: 0.0641, Val Accuracy: 0.9763
Epoch 698/1000, Train Loss: 0.0574, Train Accuracy: 0.9814, Val Loss: 0.0642, Val Accuracy: 0.9763
Epoch 699/1000, Train Loss: 0.0563, Train Accuracy: 0.9814, Val Loss: 0.0641, Val Accuracy: 0.9763
Epoch 700/1000, Train Loss: 0.0544, Train Accuracy: 0.9823, Val Loss: 0.0640, Val Accuracy: 0.9763
Epoch 701/1000, Train Loss: 0.0554, Train Accuracy: 0.9824, Val Loss: 0.0640, Val Accuracy: 0.9763
Epoch 702/1000, Train Loss: 0.0550, Train Accuracy: 0.9816, Val Loss: 0.0640, Val Accuracy: 0.9763
Epoch 703/1000, Train Loss: 0.0555, Train Accuracy: 0.9829, Val Loss: 0.0639, Val Accuracy: 0.9763
Epoch 704/1000, Train Loss: 0.0544, Train Accuracy: 0.9829, Val Loss: 0.0638, Val Accuracy: 0.9763
Epoch 705/1000, Train Loss: 0.0563, Train Accuracy: 0.9809, Val Loss: 0.0637, Val Accuracy: 0.9763
Epoch 706/1000, Train Loss: 0.0533, Train Accuracy: 0.9837, Val Loss: 0.0638, Val Accuracy: 0.9763
Epoch 707/1000, Train Loss: 0.0544, Train Accuracy: 0.9826, Val Loss: 0.0635, Val Accuracy: 0.9766
Epoch 708/1000, Train Loss: 0.0553, Train Accuracy: 0.9825, Val Loss: 0.0637, Val Accuracy: 0.9763
Epoch 709/1000, Train Loss: 0.0552, Train Accuracy: 0.9816, Val Loss: 0.0634, Val Accuracy: 0.9766
Epoch 710/1000, Train Loss: 0.0555, Train Accuracy: 0.9825, Val Loss: 0.0635, Val Accuracy: 0.9766
Epoch 711/1000, Train Loss: 0.0539, Train Accuracy: 0.9817, Val Loss: 0.0635, Val Accuracy: 0.9766
Epoch 712/1000, Train Loss: 0.0566, Train Accuracy: 0.9816, Val Loss: 0.0633, Val Accuracy: 0.9766
Epoch 713/1000, Train Loss: 0.0526, Train Accuracy: 0.9832, Val Loss: 0.0633, Val Accuracy: 0.9766
Epoch 714/1000, Train Loss: 0.0540, Train Accuracy: 0.9829, Val Loss: 0.0634, Val Accuracy: 0.9769
Epoch 715/1000, Train Loss: 0.0543, Train Accuracy: 0.9820, Val Loss: 0.0634, Val Accuracy: 0.9769
Epoch 716/1000, Train Loss: 0.0549, Train Accuracy: 0.9822, Val Loss: 0.0633, Val Accuracy: 0.9769
Epoch 717/1000, Train Loss: 0.0542, Train Accuracy: 0.9819, Val Loss: 0.0633, Val Accuracy: 0.9769
Epoch 718/1000, Train Loss: 0.0550, Train Accuracy: 0.9819, Val Loss: 0.0631, Val Accuracy: 0.9766
Epoch 719/1000, Train Loss: 0.0541, Train Accuracy: 0.9826, Val Loss: 0.0633, Val Accuracy: 0.9763
Epoch 720/1000, Train Loss: 0.0545, Train Accuracy: 0.9823, Val Loss: 0.0633, Val Accuracy: 0.9766
Epoch 721/1000, Train Loss: 0.0523, Train Accuracy: 0.9837, Val Loss: 0.0633, Val Accuracy: 0.9766
Epoch 722/1000, Train Loss: 0.0551, Train Accuracy: 0.9816, Val Loss: 0.0631, Val Accuracy: 0.9766
Epoch 723/1000, Train Loss: 0.0527, Train Accuracy: 0.9837, Val Loss: 0.0630, Val Accuracy: 0.9766
Epoch 724/1000, Train Loss: 0.0538, Train Accuracy: 0.9826, Val Loss: 0.0631, Val Accuracy: 0.9763
Epoch 725/1000, Train Loss: 0.0534, Train Accuracy: 0.9830, Val Loss: 0.0633, Val Accuracy: 0.9760
Epoch 726/1000, Train Loss: 0.0538, Train Accuracy: 0.9829, Val Loss: 0.0633, Val Accuracy: 0.9760
Epoch 727/1000, Train Loss: 0.0522, Train Accuracy: 0.9829, Val Loss: 0.0632, Val Accuracy: 0.9763
Epoch 728/1000, Train Loss: 0.0545, Train Accuracy: 0.9821, Val Loss: 0.0631, Val Accuracy: 0.9763
Epoch 729/1000, Train Loss: 0.0548, Train Accuracy: 0.9818, Val Loss: 0.0629, Val Accuracy: 0.9763
Epoch 730/1000, Train Loss: 0.0534, Train Accuracy: 0.9830, Val Loss: 0.0631, Val Accuracy: 0.9760
Epoch 731/1000, Train Loss: 0.0546, Train Accuracy: 0.9816, Val Loss: 0.0628, Val Accuracy: 0.9760
Epoch 732/1000, Train Loss: 0.0529, Train Accuracy: 0.9833, Val Loss: 0.0630, Val Accuracy: 0.9760
Epoch 733/1000, Train Loss: 0.0549, Train Accuracy: 0.9815, Val Loss: 0.0631, Val Accuracy: 0.9757
Epoch 734/1000, Train Loss: 0.0532, Train Accuracy: 0.9828, Val Loss: 0.0631, Val Accuracy: 0.9757
Epoch 735/1000, Train Loss: 0.0520, Train Accuracy: 0.9823, Val Loss: 0.0629, Val Accuracy: 0.9760
Epoch 736/1000, Train Loss: 0.0532, Train Accuracy: 0.9835, Val Loss: 0.0631, Val Accuracy: 0.9757
Epoch 737/1000, Train Loss: 0.0535, Train Accuracy: 0.9832, Val Loss: 0.0629, Val Accuracy: 0.9760
Epoch 738/1000, Train Loss: 0.0527, Train Accuracy: 0.9824, Val Loss: 0.0629, Val Accuracy: 0.9760
Epoch 739/1000, Train Loss: 0.0530, Train Accuracy: 0.9830, Val Loss: 0.0629, Val Accuracy: 0.9760
Epoch 740/1000, Train Loss: 0.0537, Train Accuracy: 0.9834, Val Loss: 0.0631, Val Accuracy: 0.9760
Epoch 741/1000, Train Loss: 0.0538, Train Accuracy: 0.9823, Val Loss: 0.0628, Val Accuracy: 0.9760
Epoch 742/1000, Train Loss: 0.0524, Train Accuracy: 0.9836, Val Loss: 0.0628, Val Accuracy: 0.9763
Epoch 743/1000, Train Loss: 0.0539, Train Accuracy: 0.9829, Val Loss: 0.0627, Val Accuracy: 0.9763
Epoch 744/1000, Train Loss: 0.0528, Train Accuracy: 0.9837, Val Loss: 0.0624, Val Accuracy: 0.9760
Epoch 745/1000, Train Loss: 0.0544, Train Accuracy: 0.9821, Val Loss: 0.0625, Val Accuracy: 0.9760
Epoch 746/1000, Train Loss: 0.0529, Train Accuracy: 0.9828, Val Loss: 0.0625, Val Accuracy: 0.9763
Epoch 747/1000, Train Loss: 0.0518, Train Accuracy: 0.9830, Val Loss: 0.0627, Val Accuracy: 0.9760
Epoch 748/1000, Train Loss: 0.0531, Train Accuracy: 0.9824, Val Loss: 0.0626, Val Accuracy: 0.9763
Epoch 749/1000, Train Loss: 0.0527, Train Accuracy: 0.9829, Val Loss: 0.0623, Val Accuracy: 0.9766
Epoch 750/1000, Train Loss: 0.0536, Train Accuracy: 0.9826, Val Loss: 0.0624, Val Accuracy: 0.9769
Epoch 751/1000, Train Loss: 0.0525, Train Accuracy: 0.9833, Val Loss: 0.0623, Val Accuracy: 0.9766
Epoch 752/1000, Train Loss: 0.0543, Train Accuracy: 0.9822, Val Loss: 0.0625, Val Accuracy: 0.9769
Epoch 753/1000, Train Loss: 0.0547, Train Accuracy: 0.9822, Val Loss: 0.0626, Val Accuracy: 0.9769
Epoch 754/1000, Train Loss: 0.0513, Train Accuracy: 0.9834, Val Loss: 0.0624, Val Accuracy: 0.9769
Epoch 755/1000, Train Loss: 0.0525, Train Accuracy: 0.9830, Val Loss: 0.0625, Val Accuracy: 0.9766
Epoch 756/1000, Train Loss: 0.0523, Train Accuracy: 0.9835, Val Loss: 0.0623, Val Accuracy: 0.9766
Epoch 757/1000, Train Loss: 0.0524, Train Accuracy: 0.9829, Val Loss: 0.0624, Val Accuracy: 0.9769
Epoch 758/1000, Train Loss: 0.0519, Train Accuracy: 0.9832, Val Loss: 0.0623, Val Accuracy: 0.9769
Epoch 759/1000, Train Loss: 0.0526, Train Accuracy: 0.9821, Val Loss: 0.0620, Val Accuracy: 0.9769
Epoch 760/1000, Train Loss: 0.0507, Train Accuracy: 0.9844, Val Loss: 0.0620, Val Accuracy: 0.9769
Epoch 761/1000, Train Loss: 0.0541, Train Accuracy: 0.9829, Val Loss: 0.0618, Val Accuracy: 0.9769
Epoch 762/1000, Train Loss: 0.0521, Train Accuracy: 0.9827, Val Loss: 0.0618, Val Accuracy: 0.9772
Epoch 763/1000, Train Loss: 0.0523, Train Accuracy: 0.9829, Val Loss: 0.0620, Val Accuracy: 0.9769
Epoch 764/1000, Train Loss: 0.0521, Train Accuracy: 0.9827, Val Loss: 0.0620, Val Accuracy: 0.9775
Epoch 765/1000, Train Loss: 0.0521, Train Accuracy: 0.9833, Val Loss: 0.0620, Val Accuracy: 0.9777
Epoch 766/1000, Train Loss: 0.0516, Train Accuracy: 0.9825, Val Loss: 0.0620, Val Accuracy: 0.9775
Epoch 767/1000, Train Loss: 0.0508, Train Accuracy: 0.9841, Val Loss: 0.0621, Val Accuracy: 0.9777
Epoch 768/1000, Train Loss: 0.0521, Train Accuracy: 0.9829, Val Loss: 0.0620, Val Accuracy: 0.9772
Epoch 769/1000, Train Loss: 0.0518, Train Accuracy: 0.9826, Val Loss: 0.0619, Val Accuracy: 0.9772
Epoch 770/1000, Train Loss: 0.0522, Train Accuracy: 0.9822, Val Loss: 0.0620, Val Accuracy: 0.9772
Epoch 771/1000, Train Loss: 0.0525, Train Accuracy: 0.9842, Val Loss: 0.0620, Val Accuracy: 0.9772
Epoch 772/1000, Train Loss: 0.0503, Train Accuracy: 0.9834, Val Loss: 0.0619, Val Accuracy: 0.9772
Epoch 773/1000, Train Loss: 0.0519, Train Accuracy: 0.9841, Val Loss: 0.0621, Val Accuracy: 0.9772
Epoch 774/1000, Train Loss: 0.0495, Train Accuracy: 0.9851, Val Loss: 0.0621, Val Accuracy: 0.9772
Epoch 775/1000, Train Loss: 0.0503, Train Accuracy: 0.9837, Val Loss: 0.0618, Val Accuracy: 0.9769
Epoch 776/1000, Train Loss: 0.0488, Train Accuracy: 0.9841, Val Loss: 0.0618, Val Accuracy: 0.9769
Epoch 777/1000, Train Loss: 0.0512, Train Accuracy: 0.9832, Val Loss: 0.0616, Val Accuracy: 0.9769
Epoch 778/1000, Train Loss: 0.0513, Train Accuracy: 0.9826, Val Loss: 0.0616, Val Accuracy: 0.9772
Epoch 779/1000, Train Loss: 0.0514, Train Accuracy: 0.9829, Val Loss: 0.0614, Val Accuracy: 0.9772
Epoch 780/1000, Train Loss: 0.0500, Train Accuracy: 0.9834, Val Loss: 0.0616, Val Accuracy: 0.9772
Epoch 781/1000, Train Loss: 0.0485, Train Accuracy: 0.9846, Val Loss: 0.0614, Val Accuracy: 0.9772
Epoch 782/1000, Train Loss: 0.0496, Train Accuracy: 0.9840, Val Loss: 0.0616, Val Accuracy: 0.9769
Epoch 783/1000, Train Loss: 0.0496, Train Accuracy: 0.9844, Val Loss: 0.0617, Val Accuracy: 0.9772
Epoch 784/1000, Train Loss: 0.0515, Train Accuracy: 0.9832, Val Loss: 0.0616, Val Accuracy: 0.9772
Epoch 785/1000, Train Loss: 0.0494, Train Accuracy: 0.9842, Val Loss: 0.0615, Val Accuracy: 0.9772
Epoch 786/1000, Train Loss: 0.0507, Train Accuracy: 0.9835, Val Loss: 0.0617, Val Accuracy: 0.9772
Epoch 787/1000, Train Loss: 0.0497, Train Accuracy: 0.9839, Val Loss: 0.0616, Val Accuracy: 0.9772
Epoch 788/1000, Train Loss: 0.0493, Train Accuracy: 0.9835, Val Loss: 0.0615, Val Accuracy: 0.9772
Epoch 789/1000, Train Loss: 0.0493, Train Accuracy: 0.9839, Val Loss: 0.0612, Val Accuracy: 0.9772
Epoch 790/1000, Train Loss: 0.0515, Train Accuracy: 0.9837, Val Loss: 0.0615, Val Accuracy: 0.9772
Epoch 791/1000, Train Loss: 0.0487, Train Accuracy: 0.9844, Val Loss: 0.0614, Val Accuracy: 0.9772
Epoch 792/1000, Train Loss: 0.0495, Train Accuracy: 0.9835, Val Loss: 0.0613, Val Accuracy: 0.9772
Epoch 793/1000, Train Loss: 0.0503, Train Accuracy: 0.9835, Val Loss: 0.0613, Val Accuracy: 0.9772
Epoch 794/1000, Train Loss: 0.0496, Train Accuracy: 0.9843, Val Loss: 0.0610, Val Accuracy: 0.9772
Epoch 795/1000, Train Loss: 0.0479, Train Accuracy: 0.9853, Val Loss: 0.0611, Val Accuracy: 0.9772
Epoch 796/1000, Train Loss: 0.0499, Train Accuracy: 0.9840, Val Loss: 0.0612, Val Accuracy: 0.9772
Epoch 797/1000, Train Loss: 0.0491, Train Accuracy: 0.9850, Val Loss: 0.0611, Val Accuracy: 0.9772
Epoch 798/1000, Train Loss: 0.0504, Train Accuracy: 0.9825, Val Loss: 0.0611, Val Accuracy: 0.9775
Epoch 799/1000, Train Loss: 0.0502, Train Accuracy: 0.9832, Val Loss: 0.0611, Val Accuracy: 0.9775
Epoch 800/1000, Train Loss: 0.0491, Train Accuracy: 0.9848, Val Loss: 0.0610, Val Accuracy: 0.9775
Epoch 801/1000, Train Loss: 0.0483, Train Accuracy: 0.9845, Val Loss: 0.0611, Val Accuracy: 0.9775
Epoch 802/1000, Train Loss: 0.0483, Train Accuracy: 0.9842, Val Loss: 0.0613, Val Accuracy: 0.9772
Epoch 803/1000, Train Loss: 0.0514, Train Accuracy: 0.9834, Val Loss: 0.0611, Val Accuracy: 0.9775
Epoch 804/1000, Train Loss: 0.0497, Train Accuracy: 0.9845, Val Loss: 0.0607, Val Accuracy: 0.9777
Epoch 805/1000, Train Loss: 0.0496, Train Accuracy: 0.9843, Val Loss: 0.0610, Val Accuracy: 0.9775
Epoch 806/1000, Train Loss: 0.0494, Train Accuracy: 0.9839, Val Loss: 0.0610, Val Accuracy: 0.9772
Epoch 807/1000, Train Loss: 0.0474, Train Accuracy: 0.9854, Val Loss: 0.0609, Val Accuracy: 0.9775
Epoch 808/1000, Train Loss: 0.0480, Train Accuracy: 0.9855, Val Loss: 0.0610, Val Accuracy: 0.9775
Epoch 809/1000, Train Loss: 0.0494, Train Accuracy: 0.9850, Val Loss: 0.0610, Val Accuracy: 0.9775
Epoch 810/1000, Train Loss: 0.0468, Train Accuracy: 0.9850, Val Loss: 0.0612, Val Accuracy: 0.9775
Epoch 811/1000, Train Loss: 0.0476, Train Accuracy: 0.9848, Val Loss: 0.0613, Val Accuracy: 0.9769
Epoch 812/1000, Train Loss: 0.0488, Train Accuracy: 0.9848, Val Loss: 0.0608, Val Accuracy: 0.9775
Epoch 813/1000, Train Loss: 0.0473, Train Accuracy: 0.9840, Val Loss: 0.0608, Val Accuracy: 0.9775
Epoch 814/1000, Train Loss: 0.0473, Train Accuracy: 0.9853, Val Loss: 0.0610, Val Accuracy: 0.9775
Epoch 815/1000, Train Loss: 0.0488, Train Accuracy: 0.9850, Val Loss: 0.0609, Val Accuracy: 0.9775
Epoch 816/1000, Train Loss: 0.0483, Train Accuracy: 0.9844, Val Loss: 0.0608, Val Accuracy: 0.9777
Epoch 817/1000, Train Loss: 0.0486, Train Accuracy: 0.9843, Val Loss: 0.0605, Val Accuracy: 0.9777
Epoch 818/1000, Train Loss: 0.0466, Train Accuracy: 0.9850, Val Loss: 0.0607, Val Accuracy: 0.9777
Epoch 819/1000, Train Loss: 0.0487, Train Accuracy: 0.9840, Val Loss: 0.0606, Val Accuracy: 0.9777
Epoch 820/1000, Train Loss: 0.0492, Train Accuracy: 0.9832, Val Loss: 0.0604, Val Accuracy: 0.9777
Epoch 821/1000, Train Loss: 0.0473, Train Accuracy: 0.9840, Val Loss: 0.0604, Val Accuracy: 0.9777
Epoch 822/1000, Train Loss: 0.0479, Train Accuracy: 0.9846, Val Loss: 0.0606, Val Accuracy: 0.9777
Epoch 823/1000, Train Loss: 0.0474, Train Accuracy: 0.9851, Val Loss: 0.0609, Val Accuracy: 0.9777
Epoch 824/1000, Train Loss: 0.0498, Train Accuracy: 0.9836, Val Loss: 0.0606, Val Accuracy: 0.9777
Epoch 825/1000, Train Loss: 0.0471, Train Accuracy: 0.9849, Val Loss: 0.0605, Val Accuracy: 0.9777
Epoch 826/1000, Train Loss: 0.0476, Train Accuracy: 0.9845, Val Loss: 0.0606, Val Accuracy: 0.9777
Epoch 827/1000, Train Loss: 0.0463, Train Accuracy: 0.9850, Val Loss: 0.0606, Val Accuracy: 0.9777
Epoch 828/1000, Train Loss: 0.0476, Train Accuracy: 0.9846, Val Loss: 0.0608, Val Accuracy: 0.9777
Epoch 829/1000, Train Loss: 0.0451, Train Accuracy: 0.9859, Val Loss: 0.0606, Val Accuracy: 0.9777
Epoch 830/1000, Train Loss: 0.0475, Train Accuracy: 0.9842, Val Loss: 0.0604, Val Accuracy: 0.9777
Epoch 831/1000, Train Loss: 0.0465, Train Accuracy: 0.9849, Val Loss: 0.0604, Val Accuracy: 0.9777
Epoch 832/1000, Train Loss: 0.0475, Train Accuracy: 0.9855, Val Loss: 0.0604, Val Accuracy: 0.9777
Epoch 833/1000, Train Loss: 0.0475, Train Accuracy: 0.9844, Val Loss: 0.0604, Val Accuracy: 0.9777
Epoch 834/1000, Train Loss: 0.0480, Train Accuracy: 0.9853, Val Loss: 0.0606, Val Accuracy: 0.9777
Epoch 835/1000, Train Loss: 0.0477, Train Accuracy: 0.9848, Val Loss: 0.0603, Val Accuracy: 0.9777
Epoch 836/1000, Train Loss: 0.0457, Train Accuracy: 0.9850, Val Loss: 0.0604, Val Accuracy: 0.9777
Epoch 837/1000, Train Loss: 0.0467, Train Accuracy: 0.9850, Val Loss: 0.0603, Val Accuracy: 0.9777
Epoch 838/1000, Train Loss: 0.0453, Train Accuracy: 0.9865, Val Loss: 0.0606, Val Accuracy: 0.9777
Epoch 839/1000, Train Loss: 0.0461, Train Accuracy: 0.9853, Val Loss: 0.0601, Val Accuracy: 0.9777
Epoch 840/1000, Train Loss: 0.0460, Train Accuracy: 0.9857, Val Loss: 0.0602, Val Accuracy: 0.9777
Epoch 841/1000, Train Loss: 0.0479, Train Accuracy: 0.9846, Val Loss: 0.0603, Val Accuracy: 0.9777
Epoch 842/1000, Train Loss: 0.0464, Train Accuracy: 0.9850, Val Loss: 0.0603, Val Accuracy: 0.9777
Epoch 843/1000, Train Loss: 0.0485, Train Accuracy: 0.9837, Val Loss: 0.0603, Val Accuracy: 0.9777
Epoch 844/1000, Train Loss: 0.0446, Train Accuracy: 0.9863, Val Loss: 0.0601, Val Accuracy: 0.9777
Epoch 845/1000, Train Loss: 0.0484, Train Accuracy: 0.9840, Val Loss: 0.0603, Val Accuracy: 0.9777
Epoch 846/1000, Train Loss: 0.0461, Train Accuracy: 0.9845, Val Loss: 0.0603, Val Accuracy: 0.9777
Epoch 847/1000, Train Loss: 0.0480, Train Accuracy: 0.9839, Val Loss: 0.0600, Val Accuracy: 0.9780
Epoch 848/1000, Train Loss: 0.0477, Train Accuracy: 0.9842, Val Loss: 0.0602, Val Accuracy: 0.9780
Epoch 849/1000, Train Loss: 0.0458, Train Accuracy: 0.9859, Val Loss: 0.0600, Val Accuracy: 0.9780
Epoch 850/1000, Train Loss: 0.0462, Train Accuracy: 0.9850, Val Loss: 0.0602, Val Accuracy: 0.9780
Epoch 851/1000, Train Loss: 0.0457, Train Accuracy: 0.9847, Val Loss: 0.0599, Val Accuracy: 0.9780
Epoch 852/1000, Train Loss: 0.0469, Train Accuracy: 0.9839, Val Loss: 0.0599, Val Accuracy: 0.9780
Epoch 853/1000, Train Loss: 0.0440, Train Accuracy: 0.9866, Val Loss: 0.0597, Val Accuracy: 0.9780
Epoch 854/1000, Train Loss: 0.0471, Train Accuracy: 0.9849, Val Loss: 0.0597, Val Accuracy: 0.9780
Epoch 855/1000, Train Loss: 0.0461, Train Accuracy: 0.9855, Val Loss: 0.0597, Val Accuracy: 0.9780
Epoch 856/1000, Train Loss: 0.0467, Train Accuracy: 0.9848, Val Loss: 0.0596, Val Accuracy: 0.9780
Epoch 857/1000, Train Loss: 0.0466, Train Accuracy: 0.9851, Val Loss: 0.0596, Val Accuracy: 0.9780
Epoch 858/1000, Train Loss: 0.0452, Train Accuracy: 0.9853, Val Loss: 0.0595, Val Accuracy: 0.9777
Epoch 859/1000, Train Loss: 0.0455, Train Accuracy: 0.9858, Val Loss: 0.0595, Val Accuracy: 0.9780
Epoch 860/1000, Train Loss: 0.0474, Train Accuracy: 0.9837, Val Loss: 0.0594, Val Accuracy: 0.9780
Epoch 861/1000, Train Loss: 0.0460, Train Accuracy: 0.9844, Val Loss: 0.0599, Val Accuracy: 0.9780
Epoch 862/1000, Train Loss: 0.0448, Train Accuracy: 0.9855, Val Loss: 0.0597, Val Accuracy: 0.9777
Epoch 863/1000, Train Loss: 0.0460, Train Accuracy: 0.9851, Val Loss: 0.0595, Val Accuracy: 0.9780
Epoch 864/1000, Train Loss: 0.0443, Train Accuracy: 0.9863, Val Loss: 0.0598, Val Accuracy: 0.9777
Epoch 865/1000, Train Loss: 0.0458, Train Accuracy: 0.9852, Val Loss: 0.0598, Val Accuracy: 0.9777
Epoch 866/1000, Train Loss: 0.0444, Train Accuracy: 0.9860, Val Loss: 0.0595, Val Accuracy: 0.9777
Epoch 867/1000, Train Loss: 0.0453, Train Accuracy: 0.9845, Val Loss: 0.0593, Val Accuracy: 0.9777
Epoch 868/1000, Train Loss: 0.0433, Train Accuracy: 0.9868, Val Loss: 0.0594, Val Accuracy: 0.9780
Epoch 869/1000, Train Loss: 0.0467, Train Accuracy: 0.9850, Val Loss: 0.0594, Val Accuracy: 0.9780
Epoch 870/1000, Train Loss: 0.0463, Train Accuracy: 0.9857, Val Loss: 0.0597, Val Accuracy: 0.9780
Epoch 871/1000, Train Loss: 0.0472, Train Accuracy: 0.9839, Val Loss: 0.0595, Val Accuracy: 0.9780
Epoch 872/1000, Train Loss: 0.0466, Train Accuracy: 0.9848, Val Loss: 0.0596, Val Accuracy: 0.9780
Epoch 873/1000, Train Loss: 0.0453, Train Accuracy: 0.9850, Val Loss: 0.0595, Val Accuracy: 0.9780
Epoch 874/1000, Train Loss: 0.0433, Train Accuracy: 0.9867, Val Loss: 0.0595, Val Accuracy: 0.9780
Epoch 875/1000, Train Loss: 0.0462, Train Accuracy: 0.9841, Val Loss: 0.0593, Val Accuracy: 0.9780
Epoch 876/1000, Train Loss: 0.0432, Train Accuracy: 0.9863, Val Loss: 0.0594, Val Accuracy: 0.9780
Epoch 877/1000, Train Loss: 0.0442, Train Accuracy: 0.9855, Val Loss: 0.0595, Val Accuracy: 0.9780
Epoch 878/1000, Train Loss: 0.0434, Train Accuracy: 0.9858, Val Loss: 0.0595, Val Accuracy: 0.9777
Epoch 879/1000, Train Loss: 0.0435, Train Accuracy: 0.9873, Val Loss: 0.0594, Val Accuracy: 0.9780
Epoch 880/1000, Train Loss: 0.0458, Train Accuracy: 0.9856, Val Loss: 0.0594, Val Accuracy: 0.9780
Epoch 881/1000, Train Loss: 0.0447, Train Accuracy: 0.9860, Val Loss: 0.0593, Val Accuracy: 0.9780
Epoch 882/1000, Train Loss: 0.0449, Train Accuracy: 0.9863, Val Loss: 0.0592, Val Accuracy: 0.9780
Epoch 883/1000, Train Loss: 0.0438, Train Accuracy: 0.9863, Val Loss: 0.0594, Val Accuracy: 0.9780
Epoch 884/1000, Train Loss: 0.0441, Train Accuracy: 0.9855, Val Loss: 0.0593, Val Accuracy: 0.9777
Epoch 885/1000, Train Loss: 0.0445, Train Accuracy: 0.9858, Val Loss: 0.0593, Val Accuracy: 0.9777
Epoch 886/1000, Train Loss: 0.0449, Train Accuracy: 0.9850, Val Loss: 0.0592, Val Accuracy: 0.9780
Epoch 887/1000, Train Loss: 0.0449, Train Accuracy: 0.9857, Val Loss: 0.0591, Val Accuracy: 0.9780
Epoch 888/1000, Train Loss: 0.0450, Train Accuracy: 0.9850, Val Loss: 0.0593, Val Accuracy: 0.9780
Epoch 889/1000, Train Loss: 0.0437, Train Accuracy: 0.9861, Val Loss: 0.0595, Val Accuracy: 0.9777
Epoch 890/1000, Train Loss: 0.0452, Train Accuracy: 0.9856, Val Loss: 0.0592, Val Accuracy: 0.9780
Epoch 891/1000, Train Loss: 0.0453, Train Accuracy: 0.9844, Val Loss: 0.0595, Val Accuracy: 0.9777
Epoch 892/1000, Train Loss: 0.0436, Train Accuracy: 0.9863, Val Loss: 0.0591, Val Accuracy: 0.9780
Epoch 893/1000, Train Loss: 0.0446, Train Accuracy: 0.9863, Val Loss: 0.0593, Val Accuracy: 0.9780
Epoch 894/1000, Train Loss: 0.0448, Train Accuracy: 0.9853, Val Loss: 0.0592, Val Accuracy: 0.9780
Epoch 895/1000, Train Loss: 0.0436, Train Accuracy: 0.9863, Val Loss: 0.0592, Val Accuracy: 0.9780
Epoch 896/1000, Train Loss: 0.0447, Train Accuracy: 0.9861, Val Loss: 0.0592, Val Accuracy: 0.9780
Epoch 897/1000, Train Loss: 0.0432, Train Accuracy: 0.9855, Val Loss: 0.0591, Val Accuracy: 0.9780
Epoch 898/1000, Train Loss: 0.0439, Train Accuracy: 0.9853, Val Loss: 0.0590, Val Accuracy: 0.9780
Epoch 899/1000, Train Loss: 0.0429, Train Accuracy: 0.9861, Val Loss: 0.0591, Val Accuracy: 0.9780
Epoch 900/1000, Train Loss: 0.0445, Train Accuracy: 0.9858, Val Loss: 0.0591, Val Accuracy: 0.9780
Epoch 901/1000, Train Loss: 0.0439, Train Accuracy: 0.9856, Val Loss: 0.0589, Val Accuracy: 0.9780
Epoch 902/1000, Train Loss: 0.0426, Train Accuracy: 0.9872, Val Loss: 0.0588, Val Accuracy: 0.9780
Epoch 903/1000, Train Loss: 0.0442, Train Accuracy: 0.9848, Val Loss: 0.0589, Val Accuracy: 0.9780
Epoch 904/1000, Train Loss: 0.0424, Train Accuracy: 0.9861, Val Loss: 0.0590, Val Accuracy: 0.9780
Epoch 905/1000, Train Loss: 0.0435, Train Accuracy: 0.9865, Val Loss: 0.0591, Val Accuracy: 0.9780
Epoch 906/1000, Train Loss: 0.0434, Train Accuracy: 0.9866, Val Loss: 0.0589, Val Accuracy: 0.9780
Epoch 907/1000, Train Loss: 0.0433, Train Accuracy: 0.9857, Val Loss: 0.0588, Val Accuracy: 0.9780
Epoch 908/1000, Train Loss: 0.0417, Train Accuracy: 0.9874, Val Loss: 0.0592, Val Accuracy: 0.9780
Epoch 909/1000, Train Loss: 0.0438, Train Accuracy: 0.9860, Val Loss: 0.0589, Val Accuracy: 0.9780
Epoch 910/1000, Train Loss: 0.0430, Train Accuracy: 0.9857, Val Loss: 0.0591, Val Accuracy: 0.9780
Epoch 911/1000, Train Loss: 0.0440, Train Accuracy: 0.9863, Val Loss: 0.0589, Val Accuracy: 0.9780
Epoch 912/1000, Train Loss: 0.0436, Train Accuracy: 0.9854, Val Loss: 0.0588, Val Accuracy: 0.9777
Epoch 913/1000, Train Loss: 0.0409, Train Accuracy: 0.9868, Val Loss: 0.0586, Val Accuracy: 0.9777
Epoch 914/1000, Train Loss: 0.0427, Train Accuracy: 0.9859, Val Loss: 0.0585, Val Accuracy: 0.9777
Epoch 915/1000, Train Loss: 0.0449, Train Accuracy: 0.9846, Val Loss: 0.0586, Val Accuracy: 0.9780
Epoch 916/1000, Train Loss: 0.0422, Train Accuracy: 0.9859, Val Loss: 0.0589, Val Accuracy: 0.9780
Epoch 917/1000, Train Loss: 0.0429, Train Accuracy: 0.9866, Val Loss: 0.0588, Val Accuracy: 0.9777
Epoch 918/1000, Train Loss: 0.0437, Train Accuracy: 0.9861, Val Loss: 0.0589, Val Accuracy: 0.9777
Epoch 919/1000, Train Loss: 0.0440, Train Accuracy: 0.9861, Val Loss: 0.0588, Val Accuracy: 0.9777
Epoch 920/1000, Train Loss: 0.0431, Train Accuracy: 0.9857, Val Loss: 0.0588, Val Accuracy: 0.9780
Epoch 921/1000, Train Loss: 0.0441, Train Accuracy: 0.9855, Val Loss: 0.0588, Val Accuracy: 0.9780
Epoch 922/1000, Train Loss: 0.0407, Train Accuracy: 0.9874, Val Loss: 0.0588, Val Accuracy: 0.9780
Epoch 923/1000, Train Loss: 0.0422, Train Accuracy: 0.9871, Val Loss: 0.0588, Val Accuracy: 0.9780
Epoch 924/1000, Train Loss: 0.0420, Train Accuracy: 0.9868, Val Loss: 0.0590, Val Accuracy: 0.9780
Epoch 925/1000, Train Loss: 0.0410, Train Accuracy: 0.9882, Val Loss: 0.0588, Val Accuracy: 0.9780
Epoch 926/1000, Train Loss: 0.0430, Train Accuracy: 0.9868, Val Loss: 0.0589, Val Accuracy: 0.9780
Epoch 927/1000, Train Loss: 0.0431, Train Accuracy: 0.9866, Val Loss: 0.0589, Val Accuracy: 0.9780
Epoch 928/1000, Train Loss: 0.0393, Train Accuracy: 0.9875, Val Loss: 0.0588, Val Accuracy: 0.9783
Epoch 929/1000, Train Loss: 0.0432, Train Accuracy: 0.9861, Val Loss: 0.0589, Val Accuracy: 0.9783
Epoch 930/1000, Train Loss: 0.0422, Train Accuracy: 0.9862, Val Loss: 0.0588, Val Accuracy: 0.9783
Epoch 931/1000, Train Loss: 0.0421, Train Accuracy: 0.9863, Val Loss: 0.0587, Val Accuracy: 0.9783
Epoch 932/1000, Train Loss: 0.0425, Train Accuracy: 0.9868, Val Loss: 0.0588, Val Accuracy: 0.9786
Epoch 933/1000, Train Loss: 0.0434, Train Accuracy: 0.9856, Val Loss: 0.0586, Val Accuracy: 0.9786
Epoch 934/1000, Train Loss: 0.0422, Train Accuracy: 0.9868, Val Loss: 0.0585, Val Accuracy: 0.9783
Epoch 935/1000, Train Loss: 0.0418, Train Accuracy: 0.9860, Val Loss: 0.0587, Val Accuracy: 0.9786
Epoch 936/1000, Train Loss: 0.0420, Train Accuracy: 0.9868, Val Loss: 0.0587, Val Accuracy: 0.9783
Epoch 937/1000, Train Loss: 0.0413, Train Accuracy: 0.9874, Val Loss: 0.0586, Val Accuracy: 0.9783
Epoch 938/1000, Train Loss: 0.0423, Train Accuracy: 0.9872, Val Loss: 0.0583, Val Accuracy: 0.9783
Epoch 939/1000, Train Loss: 0.0399, Train Accuracy: 0.9869, Val Loss: 0.0584, Val Accuracy: 0.9783
Epoch 940/1000, Train Loss: 0.0415, Train Accuracy: 0.9869, Val Loss: 0.0589, Val Accuracy: 0.9780
Epoch 941/1000, Train Loss: 0.0433, Train Accuracy: 0.9863, Val Loss: 0.0586, Val Accuracy: 0.9780
Epoch 942/1000, Train Loss: 0.0395, Train Accuracy: 0.9869, Val Loss: 0.0586, Val Accuracy: 0.9780
Epoch 943/1000, Train Loss: 0.0423, Train Accuracy: 0.9862, Val Loss: 0.0585, Val Accuracy: 0.9783
Epoch 944/1000, Train Loss: 0.0414, Train Accuracy: 0.9874, Val Loss: 0.0584, Val Accuracy: 0.9783
Epoch 945/1000, Train Loss: 0.0418, Train Accuracy: 0.9866, Val Loss: 0.0584, Val Accuracy: 0.9783
Epoch 946/1000, Train Loss: 0.0412, Train Accuracy: 0.9863, Val Loss: 0.0584, Val Accuracy: 0.9786
Epoch 947/1000, Train Loss: 0.0425, Train Accuracy: 0.9858, Val Loss: 0.0585, Val Accuracy: 0.9780
Epoch 948/1000, Train Loss: 0.0410, Train Accuracy: 0.9879, Val Loss: 0.0584, Val Accuracy: 0.9780
Epoch 949/1000, Train Loss: 0.0402, Train Accuracy: 0.9875, Val Loss: 0.0586, Val Accuracy: 0.9777
Epoch 950/1000, Train Loss: 0.0414, Train Accuracy: 0.9867, Val Loss: 0.0585, Val Accuracy: 0.9780
Epoch 951/1000, Train Loss: 0.0407, Train Accuracy: 0.9866, Val Loss: 0.0585, Val Accuracy: 0.9777
Epoch 952/1000, Train Loss: 0.0422, Train Accuracy: 0.9864, Val Loss: 0.0584, Val Accuracy: 0.9783
Epoch 953/1000, Train Loss: 0.0413, Train Accuracy: 0.9868, Val Loss: 0.0583, Val Accuracy: 0.9783
Epoch 954/1000, Train Loss: 0.0404, Train Accuracy: 0.9877, Val Loss: 0.0584, Val Accuracy: 0.9780
Epoch 955/1000, Train Loss: 0.0418, Train Accuracy: 0.9869, Val Loss: 0.0581, Val Accuracy: 0.9780
Epoch 956/1000, Train Loss: 0.0405, Train Accuracy: 0.9873, Val Loss: 0.0582, Val Accuracy: 0.9780
Epoch 957/1000, Train Loss: 0.0415, Train Accuracy: 0.9876, Val Loss: 0.0583, Val Accuracy: 0.9777
Epoch 958/1000, Train Loss: 0.0406, Train Accuracy: 0.9871, Val Loss: 0.0583, Val Accuracy: 0.9780
Epoch 959/1000, Train Loss: 0.0413, Train Accuracy: 0.9867, Val Loss: 0.0582, Val Accuracy: 0.9780
Epoch 960/1000, Train Loss: 0.0410, Train Accuracy: 0.9869, Val Loss: 0.0584, Val Accuracy: 0.9780
Epoch 961/1000, Train Loss: 0.0394, Train Accuracy: 0.9874, Val Loss: 0.0584, Val Accuracy: 0.9780
Epoch 962/1000, Train Loss: 0.0404, Train Accuracy: 0.9871, Val Loss: 0.0585, Val Accuracy: 0.9780
Epoch 963/1000, Train Loss: 0.0407, Train Accuracy: 0.9876, Val Loss: 0.0582, Val Accuracy: 0.9780
Epoch 964/1000, Train Loss: 0.0413, Train Accuracy: 0.9871, Val Loss: 0.0582, Val Accuracy: 0.9780
Epoch 965/1000, Train Loss: 0.0414, Train Accuracy: 0.9869, Val Loss: 0.0583, Val Accuracy: 0.9780
Epoch 966/1000, Train Loss: 0.0416, Train Accuracy: 0.9874, Val Loss: 0.0582, Val Accuracy: 0.9780
Epoch 967/1000, Train Loss: 0.0393, Train Accuracy: 0.9876, Val Loss: 0.0581, Val Accuracy: 0.9780
Epoch 968/1000, Train Loss: 0.0394, Train Accuracy: 0.9874, Val Loss: 0.0584, Val Accuracy: 0.9783
Epoch 969/1000, Train Loss: 0.0397, Train Accuracy: 0.9870, Val Loss: 0.0585, Val Accuracy: 0.9783
Epoch 970/1000, Train Loss: 0.0409, Train Accuracy: 0.9866, Val Loss: 0.0581, Val Accuracy: 0.9783
Epoch 971/1000, Train Loss: 0.0372, Train Accuracy: 0.9887, Val Loss: 0.0585, Val Accuracy: 0.9783
Epoch 972/1000, Train Loss: 0.0393, Train Accuracy: 0.9880, Val Loss: 0.0583, Val Accuracy: 0.9783
Epoch 973/1000, Train Loss: 0.0408, Train Accuracy: 0.9867, Val Loss: 0.0584, Val Accuracy: 0.9783
Epoch 974/1000, Train Loss: 0.0412, Train Accuracy: 0.9876, Val Loss: 0.0581, Val Accuracy: 0.9783
Epoch 975/1000, Train Loss: 0.0415, Train Accuracy: 0.9866, Val Loss: 0.0581, Val Accuracy: 0.9783
Epoch 976/1000, Train Loss: 0.0398, Train Accuracy: 0.9876, Val Loss: 0.0579, Val Accuracy: 0.9786
Epoch 977/1000, Train Loss: 0.0395, Train Accuracy: 0.9877, Val Loss: 0.0577, Val Accuracy: 0.9786
Epoch 978/1000, Train Loss: 0.0398, Train Accuracy: 0.9882, Val Loss: 0.0579, Val Accuracy: 0.9780
Epoch 979/1000, Train Loss: 0.0369, Train Accuracy: 0.9889, Val Loss: 0.0578, Val Accuracy: 0.9783
Epoch 980/1000, Train Loss: 0.0390, Train Accuracy: 0.9872, Val Loss: 0.0583, Val Accuracy: 0.9780
Epoch 981/1000, Train Loss: 0.0401, Train Accuracy: 0.9869, Val Loss: 0.0581, Val Accuracy: 0.9780
Epoch 982/1000, Train Loss: 0.0395, Train Accuracy: 0.9873, Val Loss: 0.0580, Val Accuracy: 0.9780
Epoch 983/1000, Train Loss: 0.0403, Train Accuracy: 0.9880, Val Loss: 0.0583, Val Accuracy: 0.9780
Epoch 984/1000, Train Loss: 0.0390, Train Accuracy: 0.9875, Val Loss: 0.0582, Val Accuracy: 0.9780
Epoch 985/1000, Train Loss: 0.0387, Train Accuracy: 0.9877, Val Loss: 0.0582, Val Accuracy: 0.9780
Epoch 986/1000, Train Loss: 0.0405, Train Accuracy: 0.9872, Val Loss: 0.0583, Val Accuracy: 0.9780
Epoch 987/1000, Train Loss: 0.0375, Train Accuracy: 0.9877, Val Loss: 0.0578, Val Accuracy: 0.9780
Epoch 988/1000, Train Loss: 0.0375, Train Accuracy: 0.9887, Val Loss: 0.0578, Val Accuracy: 0.9780
Epoch 989/1000, Train Loss: 0.0385, Train Accuracy: 0.9871, Val Loss: 0.0580, Val Accuracy: 0.9780
Epoch 990/1000, Train Loss: 0.0409, Train Accuracy: 0.9869, Val Loss: 0.0580, Val Accuracy: 0.9783
Epoch 991/1000, Train Loss: 0.0390, Train Accuracy: 0.9875, Val Loss: 0.0580, Val Accuracy: 0.9783
Epoch 992/1000, Train Loss: 0.0383, Train Accuracy: 0.9879, Val Loss: 0.0578, Val Accuracy: 0.9786
Epoch 993/1000, Train Loss: 0.0386, Train Accuracy: 0.9884, Val Loss: 0.0580, Val Accuracy: 0.9783
Epoch 994/1000, Train Loss: 0.0373, Train Accuracy: 0.9885, Val Loss: 0.0579, Val Accuracy: 0.9783
Epoch 995/1000, Train Loss: 0.0387, Train Accuracy: 0.9874, Val Loss: 0.0580, Val Accuracy: 0.9783
Epoch 996/1000, Train Loss: 0.0400, Train Accuracy: 0.9871, Val Loss: 0.0581, Val Accuracy: 0.9783
Epoch 997/1000, Train Loss: 0.0389, Train Accuracy: 0.9882, Val Loss: 0.0579, Val Accuracy: 0.9783
Epoch 998/1000, Train Loss: 0.0382, Train Accuracy: 0.9878, Val Loss: 0.0579, Val Accuracy: 0.9783
Epoch 999/1000, Train Loss: 0.0384, Train Accuracy: 0.9882, Val Loss: 0.0579, Val Accuracy: 0.9783
Epoch 1000/1000, Train Loss: 0.0384, Train Accuracy: 0.9880, Val Loss: 0.0580, Val Accuracy: 0.9783
In [ ]:
torch.save(model_DS_M.state_dict(), best_model_path_M)
In [ ]:
# Create the neural network classifier
clf = MLPClassifier(random_state=42)

# Assuming you have performed a grid search and obtained the best estimator
# Define the parameter grid
param_grid = {
    'hidden_layer_sizes': [(30,30), (10,10,10)],
    'solver': ['adam', 'sgd'],
    'alpha': [0.001, 0.01],
}

# Create the GridSearchCV object
grid_search = GridSearchCV(clf, param_grid, scoring='accuracy', cv=2)

# Fit the GridSearchCV object to the data
grid_search.fit(X_train_DS_MCF7_nn, y_train_DS_MCF7_nn)


# Save the best estimator in best_nn
best_nn_DS_MCF7 = grid_search.best_estimator_
print(best_nn_DS_MCF7)

c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(

MLPClassifier(alpha=0.01, hidden_layer_sizes=(30, 30), random_state=42,
              solver='sgd')

c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
¶

Test for the model drop-seq MCF7

This below is the step for testing our neural network model.

First, it is set to evaluation mode to ensure consistency during the evaluation process, and test data is converted to tensors (again to be able to work with PyTorch).

A forward pass is performed on the test data to obtain predictions. The test loss is calculated using the same loss function (BCEWithLogitsLoss) and predictions are converted to binary values by applying a sigmoid function. Finally, the number of correct predictions is counted and used to compute the test accuracy.

In [ ]:
# Load the best model
model_DS_M.load_state_dict(torch.load('best_model_final_M.pth'))

# Evaluate on test data
model_DS_M.eval()
X_test_tensor_M = torch.tensor(X_test_df_DS_MCF7.values, dtype=torch.float32)
y_test_tensor_M = torch.tensor(y_test_df_DS_MCF7.values, dtype=torch.float32).unsqueeze(1)


with torch.no_grad():
    test_output = model_DS_M(X_test_tensor_M)
    test_loss = criterion(test_output, y_test_tensor_M)
    test_predictions = torch.round(torch.sigmoid(test_output))
    test_correct = (test_predictions == y_test_tensor_M).sum().item()
    test_total = y_test_tensor_M.size(0)
    test_acc = test_correct / test_total

print(f'Test Loss: {test_loss.item():.4f}, Test Accuracy: {test_acc:.4f}')

# Test Loss: 0.0537, Test Accuracy: 0.9804

Test Loss: 0.0570, Test Accuracy: 0.9794

¶

4.3.5) KNN

In [ ]:
best_knn_DS_MCF7=train_and_evaluate_knn(X_train_DS_MCF, X_test_DS_MCF, y_train_DS_MCF, y_test_DS_MCF)

Best hyperparameters:  {'n_neighbors': 2}
Accuracy: 0.5305131761442441
F1: 0.6015303119482048
Classification Report:
              precision    recall  f1-score   support

         0.0       0.77      0.30      0.43      2565
         1.0       0.46      0.87      0.60      1761

    accuracy                           0.53      4326
   macro avg       0.61      0.58      0.52      4326
weighted avg       0.64      0.53      0.50      4326

¶

4.3.6) Logistic Regression

In [ ]:
best_logreg_DS_MCF7=train_and_evaluate_logreg(X_train_DS_MCF, X_test_DS_MCF, y_train_DS_MCF, y_test_DS_MCF)

Best hyperparameters:  {'C': 0.002, 'penalty': 'l2'}
Accuracy: 0.9759593157651411
F1: 0.9701321079839172
Classification Report:
              precision    recall  f1-score   support

         0.0       0.97      0.99      0.98      2565
         1.0       0.98      0.96      0.97      1761

    accuracy                           0.98      4326
   macro avg       0.98      0.97      0.98      4326
weighted avg       0.98      0.98      0.98      4326
In [ ]:
# try pca method to reduce number of features and use log log regression
In [ ]:
X_train_pca_DS_MCF, X_test_pca_DS_MCF, y_train_DS_MCF, y_test_DS_MCF,scaler_DS_MCF,pca_DS_MCF = prepare_data_pca(df_trans_DS_MCF)
In [ ]:
X_train_pca_DS_MCF.shape
Out[ ]:
(17300, 1869)
In [ ]:
model_logreg_mc_DROP_ver2_DS_MCF=train_and_evaluate_logreg(X_train_pca_DS_MCF, X_test_pca_DS_MCF, y_train_DS_MCF, y_test_DS_MCF)

Best hyperparameters:  {'C': 0.002, 'penalty': 'l2'}
Accuracy: 0.9759593157651411
F1: 0.9701492537313432
Classification Report:
              precision    recall  f1-score   support

         0.0       0.97      0.99      0.98      2565
         1.0       0.98      0.96      0.97      1761

    accuracy                           0.98      4326
   macro avg       0.98      0.97      0.98      4326
weighted avg       0.98      0.98      0.98      4326

¶

4.3.7) CatBoost

In [ ]:
best_catboost_DS_MCF7=train_and_evaluate_catboost(X_train_DS_MCF, X_test_DS_MCF, y_train_DS_MCF, y_test_DS_MCF)

Best hyperparameters:  {'logging_level': 'Silent'}
Accuracy: 0.9854368932038835
F1: 0.9820051413881747
Classification Report:
              precision    recall  f1-score   support

         0.0       0.98      0.99      0.99      2565
         1.0       0.99      0.98      0.98      1761

    accuracy                           0.99      4326
   macro avg       0.99      0.98      0.98      4326
weighted avg       0.99      0.99      0.99      4326
In [ ]:
new_columns_names_DS_MCF = []
for i in range(1,3001):
    name_new_DS_MCF = f'f{i}'
    new_columns_names_DS_MCF.append(name_new_DS_MCF)
X_test_pd_DS_MCF=pd.DataFrame(X_test_DS_MCF, columns=new_columns_names_DS_MCF)
X_train_pd_DS_MCF=pd.DataFrame(X_train_DS_MCF, columns=new_columns_names_DS_MCF)
In [ ]:
model_catboost_mc_DROP_ver2_DS_MCF,cols_cut_DS_MCF=train_and_evaluate_catboost2(X_train_pd_DS_MCF, X_test_pd_DS_MCF, y_train_DS_MCF, y_test_DS_MCF)

185
Best hyperparameters:  {'l2_leaf_reg': 3, 'logging_level': 'Silent', 'n_estimators': 1500}
Accuracy: 0.9870550161812298
F1: 0.984
Classification Report:
              precision    recall  f1-score   support

         0.0       0.98      0.99      0.99      2565
         1.0       0.99      0.98      0.98      1761

    accuracy                           0.99      4326
   macro avg       0.99      0.99      0.99      4326
weighted avg       0.99      0.99      0.99      4326

¶

4.4) PREDICTORS DROPSEQ HCC1806

We apply the same steps of the dataset before (see 4.1) to the DropSeq HCC1806 dataset.

In [ ]:
#load the DropSeq HCC1806 dataset
df_DS_HCC1806_norm_filt = pd.read_csv("DropSeq\HCC1806_Filtered_Normalised_3000_Data_train.txt",delimiter="\ ",engine='python',index_col=0)

df_DS_HCC1806_norm_filtT = df_DS_HCC1806_norm_filt.T

# Add the target column
df_DS_HCC1806_norm_filtT['target'] = np.where(df_DS_HCC1806_norm_filtT.index.str.contains('Hypo'), 1, 0)

#i want to split the data into training and testing data, with a balance of 70% training and 30% testing

X_df_DS_HCC1806 = df_DS_HCC1806_norm_filtT.drop('target', axis=1)
y_df_DS_HCC1806 = df_DS_HCC1806_norm_filtT['target']

X_train_df_DS_HCC1806, X_test_df_DS_HCC1806, y_train_df_DS_HCC1806, y_test_df_DS_HCC1806 = train_test_split(X_df_DS_HCC1806, y_df_DS_HCC1806, test_size=0.3, random_state=42)
In [ ]:
df_basic_DS_HCC=df_DS_HCC1806_norm_filt
df_trans_DS_HCC=df_basic_DS_HCC.T
df_trans_DS_HCC['TARGET'] = df_trans_DS_HCC.columns.to_series().apply(lambda x: 1 if 'Hypo' in x else 0)
df_trans_DS_HCC.loc[df_trans_DS_HCC.index.str.contains('Norm'), 'TARGET'] = 0
df_trans_DS_HCC.loc[df_trans_DS_HCC.index.str.contains('Hypo'), 'TARGET'] = 1
In [ ]:
X_train_DS_HCC, X_test_DS_HCC, y_train_DS_HCC, y_test_DS_HCC,scaler_DS_HCC = prepare_data(df_trans_DS_HCC)

¶

4.4.1) Support Vector Machine

We used same approach used for SmartSeq MCF7, see 4.1.1.

In [ ]:
# build SVM on DropSeq HCC1806

# Define the SVM classifier
svm = SVC(random_state=42)

# Define the parameter grid for GridSearchCV
param_grid = {'kernel': ['linear', 'rbf']}
grid_search_svm = GridSearchCV(svm, param_grid, cv=2)

# Define the leave one out cross validation
#loo = LeaveOneOut()

# Perform the nested cross validation
scores_svm = cross_val_score(grid_search_svm, X_train_df_DS_HCC1806, y_train_df_DS_HCC1806, cv=2)

# Print the mean accuracy of the nested cross validation
print("Mean accuracy of 2 fold cross validation: ", np.mean(scores_svm))

Mean accuracy of 2 fold cross validation:  0.9504724478872324
In [ ]:
# Perform GridSearchCV on the train dataset to get the best parameters and fit the model
grid_search_svm.fit(X_train_df_DS_HCC1806, y_train_df_DS_HCC1806)
print("Best kernel: ", grid_search_svm.best_params_)
print("Best accuracy: ", grid_search_svm.best_score_)
best_svm_DS_HCC1806 = grid_search_svm.best_estimator_
print("Best model: ", best_svm_DS_HCC1806)
# Test the model on the test dataset
print("Test accuracy: ", best_svm_DS_HCC1806.score(X_test_df_DS_HCC1806, y_test_df_DS_HCC1806))

Best kernel:  {'kernel': 'rbf'}
Best accuracy:  0.9504724478872324
Best model:  SVC(random_state=42)
Test accuracy:  0.9584562996594779

¶

4.4.2) Random Forest

Here we build the Random forest classifier, the strategy is the same as the other DropSeq dataset.

In this case the hypeparameters are slightly higher in the grid search but the cross validation confirms that there is no overfitting.

The performance in this dataset is the lowest among all the others, it gets a score of 92,14%.

The impressive thing in this case is that all the four genes we spotted in the exploration of the dataset (BCYRN1, RPL41, HSP90AA1, MALAT1) are present in the most important feature ranking, and this was very interesting as it showed that we were able, without further informations, to find some genes that at the end resulted very important for the predictions.

In [ ]:
#i build the random forest classifier for the DropSeq HCC1806 dataset

# Define the random forest classifier
rf = RandomForestClassifier(random_state=42)

# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [100, 150], 'max_depth': [40, 50, 70]}
grid_search = GridSearchCV(rf, param_grid, cv=5)

outer_cv = KFold(n_splits=5, shuffle=True, random_state=42)

# Perform the cross validation
scores = cross_val_score(grid_search, X_train_df_DS_HCC1806, y_train_df_DS_HCC1806, cv=outer_cv)

# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))    

# Perform GridSearchCV on the entire dataset to get the best parameters and fit the model
grid_search.fit(X_train_df_DS_HCC1806, y_train_df_DS_HCC1806)
print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
print("Best model: ", grid_search.best_estimator_)

#i want to test the model on the test dataset
print("Test accuracy: ", grid_search.best_estimator_.score(X_test_df_DS_HCC1806, y_test_df_DS_HCC1806))

# Print the feature importances
importances = grid_search.best_estimator_.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_df_DS_HCC1806.shape[1]):
    print("%d. feature %s (%f)" % (f + 1, X_train_df_DS_HCC1806.columns[indices[f]], importances[indices[f]]))

best_rf_DS_MCF7 = grid_search.best_estimator_

Mean accuracy of nested cross validation:  0.9303298399083568
Best hyperparameters:  {'max_depth': 70, 'n_estimators': 150}
Best accuracy:  0.9302334630350195
Best model:  RandomForestClassifier(max_depth=70, n_estimators=150, random_state=42)
Test accuracy:  0.9343927355278093
Feature ranking:
1. feature "NDRG1" (0.023911)
2. feature "AKR1C2" (0.023133)
3. feature "BCYRN1" (0.019082)
4. feature "IGFBP3" (0.018349)
5. feature "FGF23" (0.016695)
6. feature "HSP90AA1" (0.016592)
7. feature "LDHA" (0.016076)
8. feature "AKR1C1" (0.015522)
9. feature "HSPD1" (0.015154)
10. feature "GPM6A" (0.013662)
11. feature "WDR43" (0.012692)
12. feature "EIF3J" (0.011704)
13. feature "NCL" (0.010339)
14. feature "RPL41" (0.010255)
15. feature "P4HA1" (0.009400)
16. feature "TMSB10" (0.009368)
17. feature "HSP90AB1" (0.008751)
18. feature "CALM2" (0.008416)
19. feature "S100A10" (0.008073)
20. feature "RPS28" (0.007900)
21. feature "MALAT1" (0.007849)
22. feature "ENO1" (0.007802)
23. feature "NPM1P40" (0.007409)
24. feature "RPS27" (0.007406)
25. feature "BNIP3" (0.007076)
26. feature "HSP90B1" (0.005948)
27. feature "KRT19" (0.005588)
28. feature "B4GALT1" (0.005297)
29. feature "RPS5" (0.005291)
30. feature "PGK1" (0.005287)
31. feature "ZC3H15" (0.005089)
32. feature "FTL" (0.005048)
33. feature "H4C3" (0.004805)
34. feature "NCALD" (0.004597)
35. feature "YWHAZ" (0.004507)
36. feature "S100A11" (0.004499)
37. feature "CACNA1A" (0.004492)
38. feature "PSMA7" (0.004460)
39. feature "HNRNPU" (0.004389)
40. feature "SERF2" (0.004206)
41. feature "BTBD9" (0.004205)
42. feature "RPL35" (0.004187)
43. feature "RPLP1" (0.004092)
44. feature "ZNF302" (0.003777)
45. feature "NEAT1" (0.003733)
46. feature "MT-RNR2" (0.003594)
47. feature "RPL39" (0.003581)
48. feature "CAST" (0.003562)
49. feature "EEF2" (0.003488)
50. feature "EGLN3" (0.003421)
51. feature "RPL8" (0.003396)
52. feature "MARCKS" (0.003125)
53. feature "S100A6" (0.003095)
54. feature "AHNAK2" (0.003091)
55. feature "RPLP2" (0.003005)
56. feature "RPS3" (0.002872)
57. feature "RPS8" (0.002848)
58. feature "MT-TQ" (0.002847)
59. feature "CBX3" (0.002841)
60. feature "PRRG3" (0.002833)
61. feature "RPL12" (0.002777)
62. feature "CALR" (0.002732)
63. feature "RPS12" (0.002717)
64. feature "HSPA5" (0.002670)
65. feature "RPS2" (0.002648)
66. feature "RPS19" (0.002635)
67. feature "RPL10" (0.002626)
68. feature "GSTP1" (0.002616)
69. feature "RPS15" (0.002610)
70. feature "H1-3" (0.002476)
71. feature "RPL27A" (0.002467)
72. feature "MT2A" (0.002429)
73. feature "EZR" (0.002410)
74. feature "YBX1" (0.002402)
75. feature "RPL28" (0.002399)
76. feature "HNRNPA2B1" (0.002394)
77. feature "HEPACAM" (0.002387)
78. feature "CAV1" (0.002366)
79. feature "PDIA3" (0.002365)
80. feature "TMSB4X" (0.002336)
81. feature "RPL36" (0.002330)
82. feature "H1-5" (0.002328)
83. feature "H2AC12" (0.002326)
84. feature "PTMS" (0.002316)
85. feature "BNIP3L" (0.002314)
86. feature "GAPDH" (0.002304)
87. feature "RPL13" (0.002242)
88. feature "RPL21" (0.002229)
89. feature "MT-TV" (0.002224)
90. feature "BAP1" (0.002203)
91. feature "DNMT1" (0.002179)
92. feature "TPT1" (0.002123)
93. feature "RPL35A" (0.002097)
94. feature "POLR2L" (0.002075)
95. feature "DSP" (0.002050)
96. feature "S100A2" (0.002043)
97. feature "RAC1" (0.002010)
98. feature "RPL18" (0.002003)
99. feature "FOSL1" (0.001976)
100. feature "C19orf53" (0.001948)
101. feature "TPI1" (0.001940)
102. feature "DEK" (0.001934)
103. feature "HNRNPM" (0.001907)
104. feature "ATP5MG" (0.001885)
105. feature "ATP5F1E" (0.001871)
106. feature "RPS14" (0.001855)
107. feature "KCNJ3" (0.001843)
108. feature "CENPF" (0.001808)
109. feature "RPSA" (0.001802)
110. feature "CNNM2" (0.001797)
111. feature "RPL37A" (0.001788)
112. feature "MKI67" (0.001786)
113. feature "H1-1" (0.001777)
114. feature "PKM" (0.001763)
115. feature "RPL37" (0.001758)
116. feature "STMN1" (0.001731)
117. feature "ELOB" (0.001722)
118. feature "ATAD2" (0.001684)
119. feature "TRIM44" (0.001673)
120. feature "GPI" (0.001665)
121. feature "H1-2" (0.001611)
122. feature "EEF1A1" (0.001609)
123. feature "HMGA1" (0.001601)
124. feature "MT-RNR1" (0.001596)
125. feature "MT1X" (0.001592)
126. feature "APLP2" (0.001586)
127. feature "PLEC" (0.001586)
128. feature "UQCRQ" (0.001552)
129. feature "TOP2A" (0.001551)
130. feature "NFE2L2" (0.001551)
131. feature "FOXP1" (0.001526)
132. feature "C4orf3" (0.001500)
133. feature "SFN" (0.001477)
134. feature "TPX2" (0.001454)
135. feature "PABPC1" (0.001439)
136. feature "UQCR11" (0.001401)
137. feature "RPS21" (0.001400)
138. feature "RPL18A" (0.001381)
139. feature "PLOD2" (0.001370)
140. feature "H1-4" (0.001358)
141. feature "H4C5" (0.001353)
142. feature "HSPB1" (0.001352)
143. feature "NDUFB2" (0.001342)
144. feature "KRT7" (0.001336)
145. feature "BRD4" (0.001333)
146. feature "ACTG1" (0.001326)
147. feature "NRBP1" (0.001309)
148. feature "KIF5B" (0.001302)
149. feature "PPDPF" (0.001300)
150. feature "ANXA1" (0.001290)
151. feature "KCNJ2" (0.001279)
152. feature "PDIA6" (0.001247)
153. feature "HDGF" (0.001237)
154. feature "RTF1" (0.001230)
155. feature "HMGB2" (0.001215)
156. feature "UBC" (0.001186)
157. feature "SLC2A1" (0.001176)
158. feature "KRT13" (0.001175)
159. feature "ANP32B" (0.001174)
160. feature "GDPGP1" (0.001170)
161. feature "FABP5" (0.001157)
162. feature "ZBTB20" (0.001156)
163. feature "LAMB3" (0.001139)
164. feature "GBP1P1" (0.001133)
165. feature "NMD3" (0.001128)
166. feature "COPS9" (0.001114)
167. feature "NEDD4L" (0.001111)
168. feature "MT-CO2" (0.001111)
169. feature "TSR1" (0.001099)
170. feature "LINC01304" (0.001085)
171. feature "PPP1R14B" (0.001076)
172. feature "GOLGA4" (0.001075)
173. feature "H3-3A" (0.001056)
174. feature "AHNAK" (0.001043)
175. feature "YBX3" (0.001043)
176. feature "PFN1" (0.001041)
177. feature "OAZ1" (0.001030)
178. feature "NDUFB10" (0.001024)
179. feature "ATP1A1" (0.001014)
180. feature "RPS29" (0.001014)
181. feature "CAVIN1" (0.001008)
182. feature "H3C2" (0.000985)
183. feature "ATP5ME" (0.000979)
184. feature "MARCHF6" (0.000970)
185. feature "HSBP1" (0.000968)
186. feature "KDM1A" (0.000957)
187. feature "KRT14" (0.000955)
188. feature "SLC25A48" (0.000949)
189. feature "MT-ND4L" (0.000943)
190. feature "MZT2B" (0.000922)
191. feature "KRT5" (0.000912)
192. feature "PPIG" (0.000910)
193. feature "BTN3A2" (0.000909)
194. feature "METTL26" (0.000886)
195. feature "H2AC11" (0.000879)
196. feature "CAMK2N1" (0.000877)
197. feature "MYH9" (0.000877)
198. feature "MT-CO3" (0.000848)
199. feature "UQCC2" (0.000840)
200. feature "GTF3A" (0.000838)
201. feature "MT-ND4" (0.000834)
202. feature "CAP1" (0.000831)
203. feature "LRRC59" (0.000826)
204. feature "ATOX1" (0.000817)
205. feature "TAF15" (0.000816)
206. feature "CCDC34" (0.000813)
207. feature "MYL12B" (0.000793)
208. feature "DDIT4" (0.000789)
209. feature "HES1" (0.000788)
210. feature "POLR2A" (0.000787)
211. feature "GNAS" (0.000784)
212. feature "ERO1A" (0.000783)
213. feature "RPL27" (0.000765)
214. feature "TMA7" (0.000762)
215. feature "MRPL33" (0.000760)
216. feature "CAVIN3" (0.000759)
217. feature "RPS6KA6" (0.000755)
218. feature "MT-ND6" (0.000735)
219. feature "GUK1" (0.000734)
220. feature "NCOR2" (0.000730)
221. feature "RPS26" (0.000728)
222. feature "SHOX" (0.000727)
223. feature "GPX4" (0.000722)
224. feature "PDLIM1" (0.000719)
225. feature "MLLT3" (0.000715)
226. feature "NCOR1" (0.000708)
227. feature "RAP2B" (0.000707)
228. feature "DAAM1" (0.000705)
229. feature "MRPL55" (0.000703)
230. feature "ANP32A" (0.000701)
231. feature "KRT10" (0.000700)
232. feature "CEP55" (0.000698)
233. feature "CAMSAP2" (0.000696)
234. feature "POLR2J" (0.000696)
235. feature "TCEAL4" (0.000691)
236. feature "ZHX1" (0.000690)
237. feature "YTHDC1" (0.000686)
238. feature "CLIP1" (0.000674)
239. feature "PAQR7" (0.000670)
240. feature "CSTB" (0.000660)
241. feature "RBX1" (0.000649)
242. feature "CDK2AP1" (0.000646)
243. feature "MT-CYB" (0.000645)
244. feature "CSTA" (0.000641)
245. feature "JUP" (0.000630)
246. feature "LUZP1" (0.000628)
247. feature "SYT14" (0.000628)
248. feature "SOX4" (0.000628)
249. feature "RANGAP1" (0.000627)
250. feature "MAFF" (0.000624)
251. feature "CNN3" (0.000623)
252. feature "NUDT1" (0.000618)
253. feature "MZT2A" (0.000617)
254. feature "FLNA" (0.000614)
255. feature "HSPA4" (0.000613)
256. feature "MPHOSPH6" (0.000611)
257. feature "APOOL" (0.000610)
258. feature "EPPK1" (0.000610)
259. feature "JUN" (0.000607)
260. feature "H2AZ2" (0.000603)
261. feature "MT-ND5" (0.000594)
262. feature "HCFC1R1" (0.000594)
263. feature "H2BC4" (0.000591)
264. feature "MAP3K13" (0.000584)
265. feature "ATP2A2" (0.000582)
266. feature "CD47" (0.000576)
267. feature "HCFC1" (0.000574)
268. feature "SIVA1" (0.000563)
269. feature "BOD1L1" (0.000559)
270. feature "TAOK3" (0.000558)
271. feature "DDX54" (0.000556)
272. feature "FOSL2" (0.000553)
273. feature "MT-ND1" (0.000552)
274. feature "MTA2" (0.000551)
275. feature "PAIP2" (0.000550)
276. feature "SMARCC1" (0.000548)
277. feature "LIMCH1" (0.000547)
278. feature "HMGN3" (0.000546)
279. feature "TSPO" (0.000540)
280. feature "RPL7A" (0.000538)
281. feature "SENP7" (0.000535)
282. feature "SNRNP70" (0.000532)
283. feature "FAM107B" (0.000530)
284. feature "NMT2" (0.000528)
285. feature "LMNB2" (0.000527)
286. feature "PRRC2A" (0.000527)
287. feature "LARP7" (0.000526)
288. feature "TMEM160" (0.000524)
289. feature "LBH" (0.000523)
290. feature "PRMT2" (0.000523)
291. feature "CTNND1" (0.000522)
292. feature "PDCD4" (0.000520)
293. feature "H2BC9" (0.000520)
294. feature "OPTN" (0.000518)
295. feature "PLBD2" (0.000518)
296. feature "COTL1" (0.000518)
297. feature "PRR11" (0.000512)
298. feature "KIF3B" (0.000508)
299. feature "MCM4" (0.000502)
300. feature "SOS1" (0.000501)
301. feature "ARID5B" (0.000498)
302. feature "CAPN1" (0.000494)
303. feature "BRMS1" (0.000494)
304. feature "MT1E" (0.000493)
305. feature "FAM83A" (0.000491)
306. feature "HSPBP1" (0.000491)
307. feature "ZCRB1" (0.000488)
308. feature "CARMIL1" (0.000483)
309. feature "TAGLN2" (0.000480)
310. feature "SIGMAR1" (0.000479)
311. feature "FAM162A" (0.000477)
312. feature "MT-ND2" (0.000476)
313. feature "PDXK" (0.000473)
314. feature "HK1" (0.000470)
315. feature "CDK2AP2" (0.000470)
316. feature "MT-CO1" (0.000468)
317. feature "CCNI" (0.000464)
318. feature "TCEAL9" (0.000462)
319. feature "OIP5-AS1" (0.000461)
320. feature "DRAP1" (0.000457)
321. feature "RBM23" (0.000455)
322. feature "YKT6" (0.000453)
323. feature "H2AC16" (0.000448)
324. feature "CFAP251" (0.000448)
325. feature "CTBP2" (0.000445)
326. feature "RNF20" (0.000445)
327. feature "MT-ATP6" (0.000444)
328. feature "CLIC1" (0.000441)
329. feature "AMOTL1" (0.000441)
330. feature "C4orf48" (0.000441)
331. feature "FEM1A" (0.000440)
332. feature "PHGDH" (0.000440)
333. feature "MAP7" (0.000440)
334. feature "ARF3" (0.000440)
335. feature "PFKP" (0.000439)
336. feature "GNB1" (0.000439)
337. feature "SAMD4A" (0.000439)
338. feature "PFKFB3" (0.000437)
339. feature "MRPL14" (0.000436)
340. feature "LEO1" (0.000436)
341. feature "ZNF148" (0.000435)
342. feature "ARHGDIA" (0.000433)
343. feature "ITGA3" (0.000431)
344. feature "SLC25A37" (0.000431)
345. feature "TOP2B" (0.000430)
346. feature "MT-ATP8" (0.000430)
347. feature "CDKN3" (0.000429)
348. feature "DCTN1" (0.000429)
349. feature "CAPZB" (0.000428)
350. feature "SETD2" (0.000427)
351. feature "ANAPC16" (0.000425)
352. feature "METRN" (0.000425)
353. feature "C12orf75" (0.000423)
354. feature "PUS7" (0.000421)
355. feature "CFAP36" (0.000420)
356. feature "AK4" (0.000419)
357. feature "BRI3" (0.000419)
358. feature "AKT1S1" (0.000418)
359. feature "MXI1" (0.000415)
360. feature "PGAM1" (0.000414)
361. feature "SMARCB1" (0.000414)
362. feature "TIMP3" (0.000414)
363. feature "RRAS" (0.000413)
364. feature "RAB13" (0.000413)
365. feature "MTND1P23" (0.000412)
366. feature "POLE4" (0.000412)
367. feature "UPF3B" (0.000411)
368. feature "S100A13" (0.000411)
369. feature "FUBP3" (0.000410)
370. feature "RBFOX2" (0.000409)
371. feature "PRDM2" (0.000409)
372. feature "RFC1" (0.000406)
373. feature "CDK5RAP2" (0.000405)
374. feature "PTGR1" (0.000405)
375. feature "KRT17" (0.000405)
376. feature "SNHG9" (0.000405)
377. feature "ZCCHC9" (0.000404)
378. feature "LRRFIP2" (0.000404)
379. feature "LGALS1" (0.000404)
380. feature "IRAK1" (0.000403)
381. feature "KMT2D" (0.000402)
382. feature "ATN1" (0.000399)
383. feature "TK1" (0.000395)
384. feature "RRS1" (0.000394)
385. feature "FAM120A" (0.000394)
386. feature "RB1CC1" (0.000393)
387. feature "GOLGA3" (0.000393)
388. feature "KAT7" (0.000392)
389. feature "NUP62" (0.000392)
390. feature "FGFR1OP2" (0.000389)
391. feature "PAQR8" (0.000388)
392. feature "STC2" (0.000388)
393. feature "SNRPA" (0.000385)
394. feature "SEPTIN9" (0.000384)
395. feature "KLF6" (0.000382)
396. feature "SLC6A8" (0.000380)
397. feature "GIPC1" (0.000377)
398. feature "BRCC3" (0.000375)
399. feature "SMIM10L1" (0.000375)
400. feature "IGFL2-AS1" (0.000373)
401. feature "CCDC50" (0.000371)
402. feature "C12orf57" (0.000369)
403. feature "REST" (0.000368)
404. feature "CMAS" (0.000368)
405. feature "PSIP1" (0.000365)
406. feature "RHOA" (0.000364)
407. feature "DNMT3A" (0.000364)
408. feature "PSAP" (0.000360)
409. feature "TAF13" (0.000359)
410. feature "ID1" (0.000359)
411. feature "KIAA1522" (0.000359)
412. feature "NDUFB1" (0.000359)
413. feature "LINC01133" (0.000358)
414. feature "ODF2" (0.000355)
415. feature "MT-ND3" (0.000355)
416. feature "COX14" (0.000354)
417. feature "RGPD4-AS1" (0.000352)
418. feature "PPP4R2" (0.000352)
419. feature "SEM1" (0.000351)
420. feature "TOLLIP" (0.000351)
421. feature "COX8A" (0.000350)
422. feature "VIT" (0.000348)
423. feature "ING2" (0.000347)
424. feature "IFI27L2" (0.000346)
425. feature "ROCK2" (0.000346)
426. feature "TMEM141" (0.000345)
427. feature "HNRNPA1" (0.000345)
428. feature "TMEM238" (0.000344)
429. feature "CDA" (0.000344)
430. feature "CMIP" (0.000343)
431. feature "PHF3" (0.000342)
432. feature "TNIP1" (0.000342)
433. feature "MRPS33" (0.000340)
434. feature "DLD" (0.000340)
435. feature "MSANTD3" (0.000340)
436. feature "DDX23" (0.000339)
437. feature "ZFP36L1" (0.000337)
438. feature "ARID1A" (0.000336)
439. feature "FLOT1" (0.000336)
440. feature "DTYMK" (0.000335)
441. feature "ZNF316" (0.000334)
442. feature "SART1" (0.000331)
443. feature "NIBAN2" (0.000331)
444. feature "EHD2" (0.000329)
445. feature "MIR663AHG" (0.000329)
446. feature "ZMYND8" (0.000327)
447. feature "CALB1" (0.000327)
448. feature "CCDC167" (0.000327)
449. feature "PCBD1" (0.000326)
450. feature "RAB4A" (0.000324)
451. feature "LAGE3" (0.000323)
452. feature "GOLGA5" (0.000322)
453. feature "THRB" (0.000322)
454. feature "NIN" (0.000321)
455. feature "MT-TS1" (0.000318)
456. feature "LINC02367" (0.000317)
457. feature "RAB11B" (0.000317)
458. feature "TF" (0.000316)
459. feature "ISOC2" (0.000316)
460. feature "KMT5B" (0.000314)
461. feature "JOSD2" (0.000312)
462. feature "PRR34-AS1" (0.000312)
463. feature "CD151" (0.000311)
464. feature "WBP4" (0.000311)
465. feature "KEAP1" (0.000310)
466. feature "DCTN2" (0.000309)
467. feature "PPP2R1A" (0.000309)
468. feature "AXL" (0.000308)
469. feature "CNOT6L" (0.000308)
470. feature "SGO2" (0.000305)
471. feature "SERINC2" (0.000305)
472. feature "COMMD6" (0.000305)
473. feature "SEC61G" (0.000305)
474. feature "WIPF2" (0.000303)
475. feature "UBE2Q2" (0.000303)
476. feature "EMP2" (0.000302)
477. feature "LSM14A" (0.000300)
478. feature "EFNA2" (0.000299)
479. feature "IPO9" (0.000299)
480. feature "THOC7" (0.000299)
481. feature "SLC4A1" (0.000297)
482. feature "FOXRED2" (0.000297)
483. feature "CCDC88A" (0.000297)
484. feature "PROSER1" (0.000295)
485. feature "MACC1" (0.000295)
486. feature "GATA3" (0.000294)
487. feature "TCF20" (0.000293)
488. feature "VRK3" (0.000293)
489. feature "ZBTB38" (0.000293)
490. feature "SCRIB" (0.000292)
491. feature "RGS10" (0.000291)
492. feature "CDC42BPA" (0.000291)
493. feature "CENPB" (0.000291)
494. feature "SPRR1B" (0.000290)
495. feature "MORN2" (0.000290)
496. feature "PALLD" (0.000290)
497. feature "FAM20B" (0.000289)
498. feature "RHOD" (0.000289)
499. feature "PRR14L" (0.000288)
500. feature "MDC1" (0.000288)
501. feature "CIRBP" (0.000287)
502. feature "EPAS1" (0.000287)
503. feature "DIO2" (0.000287)
504. feature "DCAF7" (0.000286)
505. feature "MIR4458HG" (0.000286)
506. feature "PPL" (0.000285)
507. feature "STRN4" (0.000285)
508. feature "LSM4" (0.000285)
509. feature "NAB1" (0.000284)
510. feature "NDUFA2" (0.000284)
511. feature "H19" (0.000283)
512. feature "SMC6" (0.000283)
513. feature "HOXB7" (0.000283)
514. feature "ARL13B" (0.000282)
515. feature "AGRN" (0.000282)
516. feature "DNM2" (0.000281)
517. feature "SYNJ2" (0.000280)
518. feature "CCDC57" (0.000279)
519. feature "FBXW5" (0.000278)
520. feature "ADAM15" (0.000278)
521. feature "TNS4" (0.000278)
522. feature "RIN2" (0.000277)
523. feature "AP1M1" (0.000277)
524. feature "ZFHX3" (0.000276)
525. feature "STT3A" (0.000275)
526. feature "ZNF268" (0.000274)
527. feature "MAP3K4" (0.000273)
528. feature "UBE2C" (0.000273)
529. feature "PKP4" (0.000272)
530. feature "AKR1A1" (0.000272)
531. feature "EGR1" (0.000272)
532. feature "HMGN5" (0.000270)
533. feature "MAPK14" (0.000270)
534. feature "SLPI" (0.000268)
535. feature "MT-TY" (0.000268)
536. feature "EPB41L1" (0.000268)
537. feature "PCBP1" (0.000267)
538. feature "PTOV1" (0.000266)
539. feature "UCA1" (0.000266)
540. feature "SLC2A4RG" (0.000266)
541. feature "NEUROD1" (0.000265)
542. feature "MAST4" (0.000265)
543. feature "CANT1" (0.000265)
544. feature "TPD52L1" (0.000264)
545. feature "SMDT1" (0.000263)
546. feature "TWNK" (0.000262)
547. feature "EPB41L2" (0.000262)
548. feature "TMEM52B" (0.000260)
549. feature "ARHGEF7" (0.000260)
550. feature "PHLDB2" (0.000260)
551. feature "KCNJ15" (0.000260)
552. feature "TTC19" (0.000259)
553. feature "LETM1" (0.000259)
554. feature "RNF10" (0.000258)
555. feature "DYNC2I2" (0.000258)
556. feature "NEUROD2" (0.000258)
557. feature "HRAS" (0.000256)
558. feature "SAMD4B" (0.000254)
559. feature "ITSN1" (0.000254)
560. feature "ZNF688" (0.000253)
561. feature "ZNF146" (0.000251)
562. feature "ZBTB45" (0.000251)
563. feature "MYH14" (0.000250)
564. feature "SLC25A1" (0.000249)
565. feature "CREB1" (0.000249)
566. feature "NUDT14" (0.000248)
567. feature "MED29" (0.000245)
568. feature "GNL1" (0.000245)
569. feature "MKNK1" (0.000244)
570. feature "RAB35" (0.000244)
571. feature "PLAAT3" (0.000244)
572. feature "BRK1" (0.000244)
573. feature "MGMT" (0.000243)
574. feature "AKR7A2" (0.000243)
575. feature "SCNM1" (0.000242)
576. feature "LAMTOR2" (0.000242)
577. feature "UBE2I" (0.000242)
578. feature "YTHDF3" (0.000242)
579. feature "MNS1" (0.000241)
580. feature "CPEB1" (0.000241)
581. feature "MKNK2" (0.000241)
582. feature "IRF2BPL" (0.000240)
583. feature "SCP2" (0.000240)
584. feature "RPSAP48" (0.000240)
585. feature "CDC42EP1" (0.000239)
586. feature "TUBA1C" (0.000237)
587. feature "RRM1" (0.000237)
588. feature "POLR3A" (0.000237)
589. feature "SNHG6" (0.000236)
590. feature "ANGPTL4" (0.000236)
591. feature "NARF" (0.000236)
592. feature "FAM114A1" (0.000235)
593. feature "FRY" (0.000234)
594. feature "DDIT3" (0.000233)
595. feature "UACA" (0.000233)
596. feature "DPCD" (0.000233)
597. feature "MAP2" (0.000233)
598. feature "HS6ST1" (0.000232)
599. feature "TNNT1" (0.000232)
600. feature "RASGEF1B" (0.000232)
601. feature "ORC4" (0.000231)
602. feature "PLCXD2" (0.000231)
603. feature "RP9" (0.000231)
604. feature "MRFAP1" (0.000231)
605. feature "CLDN11" (0.000230)
606. feature "ARHGEF26" (0.000230)
607. feature "CAV2" (0.000230)
608. feature "USP53" (0.000229)
609. feature "PSMA5" (0.000229)
610. feature "ZNF462" (0.000229)
611. feature "TTL" (0.000229)
612. feature "UIMC1" (0.000229)
613. feature "MLXIP" (0.000228)
614. feature "TSR3" (0.000228)
615. feature "YTHDF1" (0.000227)
616. feature "NEIL3" (0.000227)
617. feature "NUMA1" (0.000227)
618. feature "LIMS1" (0.000227)
619. feature "ARFGEF3" (0.000227)
620. feature "LYPD4" (0.000227)
621. feature "NDUFC1" (0.000226)
622. feature "HMGB1P20" (0.000225)
623. feature "PRPF31" (0.000225)
624. feature "RAB3GAP1" (0.000225)
625. feature "ITSN2" (0.000225)
626. feature "CYB5R3" (0.000224)
627. feature "NDUFB4" (0.000224)
628. feature "ARL2" (0.000224)
629. feature "ZFC3H1" (0.000224)
630. feature "JUND" (0.000223)
631. feature "EXOC7" (0.000223)
632. feature "MRPS6" (0.000223)
633. feature "SH3BGRL" (0.000223)
634. feature "CHTOP" (0.000223)
635. feature "MYO1C" (0.000222)
636. feature "ZNF609" (0.000221)
637. feature "SKI" (0.000219)
638. feature "EIF5AL1" (0.000218)
639. feature "MBD2" (0.000218)
640. feature "NCAM1" (0.000218)
641. feature "FAM126B" (0.000217)
642. feature "SLC25A23" (0.000217)
643. feature "MCM5" (0.000216)
644. feature "INHBA" (0.000215)
645. feature "RAD54L2" (0.000215)
646. feature "TCF3" (0.000215)
647. feature "H2AX" (0.000215)
648. feature "CEP83" (0.000215)
649. feature "CEP250" (0.000215)
650. feature "C1orf21" (0.000215)
651. feature "NCLN" (0.000215)
652. feature "RELA" (0.000214)
653. feature "TAF10" (0.000214)
654. feature "DGKD" (0.000214)
655. feature "STX2" (0.000214)
656. feature "SLMAP" (0.000214)
657. feature "PPARA" (0.000213)
658. feature "MAP2K3" (0.000213)
659. feature "GTF3C5" (0.000213)
660. feature "SLC1A5" (0.000212)
661. feature "ZC3H4" (0.000212)
662. feature "CBX4" (0.000212)
663. feature "GADD45A" (0.000212)
664. feature "BMPR1B" (0.000212)
665. feature "RAB12" (0.000211)
666. feature "ST14" (0.000211)
667. feature "DHX40" (0.000210)
668. feature "EID1" (0.000210)
669. feature "ENTR1" (0.000210)
670. feature "PUSL1" (0.000210)
671. feature "LINC02511" (0.000210)
672. feature "TNFRSF12A" (0.000210)
673. feature "FBRSL1" (0.000210)
674. feature "GNAI2" (0.000209)
675. feature "GNG5" (0.000209)
676. feature "PGLS" (0.000209)
677. feature "OSBPL3" (0.000209)
678. feature "MOB3A" (0.000209)
679. feature "DUSP23" (0.000209)
680. feature "CHCHD7" (0.000208)
681. feature "CAPNS1" (0.000207)
682. feature "GRK2" (0.000207)
683. feature "PLCE1" (0.000206)
684. feature "LINC02762" (0.000206)
685. feature "DNAAF5" (0.000206)
686. feature "PIAS2" (0.000206)
687. feature "MCM3AP" (0.000205)
688. feature "MINK1" (0.000205)
689. feature "NEK2" (0.000204)
690. feature "RNF122" (0.000204)
691. feature "SLC25A5" (0.000204)
692. feature "NACA4P" (0.000204)
693. feature "SPINT1-AS1" (0.000203)
694. feature "ZNF318" (0.000202)
695. feature "DPM3" (0.000202)
696. feature "CTSZ" (0.000202)
697. feature "SNAPC1" (0.000201)
698. feature "RIPK1" (0.000201)
699. feature "CREB3L2" (0.000201)
700. feature "INO80E" (0.000200)
701. feature "ZFAS1" (0.000200)
702. feature "POLR2F" (0.000200)
703. feature "CIAO2A" (0.000200)
704. feature "SELENOH" (0.000199)
705. feature "EHBP1L1" (0.000199)
706. feature "MXRA5" (0.000199)
707. feature "ESCO1" (0.000199)
708. feature "UBP1" (0.000198)
709. feature "FOXO3" (0.000198)
710. feature "PLK1" (0.000198)
711. feature "WDR54" (0.000198)
712. feature "PMEPA1" (0.000198)
713. feature "FAM76B" (0.000198)
714. feature "METTL5" (0.000197)
715. feature "CETN3" (0.000197)
716. feature "TCEAL3" (0.000197)
717. feature "TBC1D20" (0.000197)
718. feature "DGUOK" (0.000197)
719. feature "ERRFI1" (0.000197)
720. feature "BICD1" (0.000197)
721. feature "VMA21" (0.000194)
722. feature "HIF3A" (0.000194)
723. feature "CASK" (0.000193)
724. feature "NDUFA8" (0.000193)
725. feature "PPP1R21" (0.000193)
726. feature "SCAF8" (0.000193)
727. feature "SPG21" (0.000193)
728. feature "GSS" (0.000192)
729. feature "PAPSS1" (0.000192)
730. feature "CLDN4" (0.000192)
731. feature "ITPR3" (0.000192)
732. feature "TEX261" (0.000191)
733. feature "NEDD1" (0.000191)
734. feature "DYNLT3" (0.000191)
735. feature "MORC3" (0.000190)
736. feature "TEDC2-AS1" (0.000189)
737. feature "GADD45B" (0.000189)
738. feature "BIK" (0.000189)
739. feature "PIN4" (0.000189)
740. feature "CHPF" (0.000188)
741. feature "C5orf38" (0.000187)
742. feature "FOXP4" (0.000187)
743. feature "MDFI" (0.000186)
744. feature "L3MBTL2" (0.000186)
745. feature "LINC01234" (0.000185)
746. feature "CLIC4" (0.000185)
747. feature "FGD4" (0.000185)
748. feature "TCEAL1" (0.000185)
749. feature "LINC02582" (0.000185)
750. feature "NAGK" (0.000184)
751. feature "LEMD3" (0.000184)
752. feature "BACH1" (0.000184)
753. feature "MYO5C" (0.000183)
754. feature "SRI" (0.000183)
755. feature "ZDHHC12" (0.000183)
756. feature "PLSCR1" (0.000183)
757. feature "CUL4B" (0.000183)
758. feature "MTUS1" (0.000182)
759. feature "IER3IP1" (0.000182)
760. feature "SNAPC3" (0.000182)
761. feature "RBSN" (0.000182)
762. feature "APPL1" (0.000180)
763. feature "CBWD3" (0.000180)
764. feature "ZNF511" (0.000180)
765. feature "SECISBP2" (0.000180)
766. feature "CDT1" (0.000180)
767. feature "PTPRE" (0.000180)
768. feature "SRA1" (0.000180)
769. feature "RBAK" (0.000180)
770. feature "FAM200B" (0.000179)
771. feature "B3GAT2" (0.000179)
772. feature "MAB21L3" (0.000179)
773. feature "MBD6" (0.000179)
774. feature "FCHO2" (0.000179)
775. feature "RNF4" (0.000178)
776. feature "MRPS2" (0.000178)
777. feature "IRS1" (0.000178)
778. feature "TNKS1BP1" (0.000178)
779. feature "CBX6" (0.000178)
780. feature "RAB31" (0.000178)
781. feature "SLITRK5" (0.000178)
782. feature "INSR" (0.000177)
783. feature "PHRF1" (0.000177)
784. feature "BNC1" (0.000177)
785. feature "ZBED4" (0.000177)
786. feature "MSR1" (0.000176)
787. feature "TMSB4XP4" (0.000175)
788. feature "HIVEP3" (0.000175)
789. feature "HMGCS1" (0.000175)
790. feature "PMPCA" (0.000174)
791. feature "AVPI1" (0.000174)
792. feature "EIF3F" (0.000174)
793. feature "NR2F6" (0.000174)
794. feature "PPIH" (0.000174)
795. feature "ANKRD40" (0.000173)
796. feature "HERC1" (0.000173)
797. feature "ERF" (0.000173)
798. feature "SHKBP1" (0.000173)
799. feature "ATXN2L" (0.000173)
800. feature "MPDU1" (0.000173)
801. feature "ESRP2" (0.000173)
802. feature "LINC01503" (0.000172)
803. feature "WDR82" (0.000171)
804. feature "MAGEA4" (0.000171)
805. feature "RPGRIP1L" (0.000171)
806. feature "VEGFB" (0.000171)
807. feature "QSOX1" (0.000170)
808. feature "ZNF324" (0.000170)
809. feature "LINC02345" (0.000170)
810. feature "MED25" (0.000169)
811. feature "MAD2L2" (0.000169)
812. feature "HES4" (0.000169)
813. feature "GFOD1" (0.000169)
814. feature "RAB5A" (0.000169)
815. feature "TCAF1" (0.000168)
816. feature "ZNF385A" (0.000167)
817. feature "GDE1" (0.000167)
818. feature "TRMT5" (0.000167)
819. feature "EPS8L2" (0.000167)
820. feature "TUBB6" (0.000167)
821. feature "AMFR" (0.000167)
822. feature "ZMIZ1" (0.000166)
823. feature "KIF16B" (0.000166)
824. feature "TNFSF13B" (0.000166)
825. feature "EEF2K" (0.000166)
826. feature "MAD2L1" (0.000165)
827. feature "AGBL5" (0.000165)
828. feature "AP2A1" (0.000165)
829. feature "WDR18" (0.000165)
830. feature "ANXA3" (0.000164)
831. feature "GDI1" (0.000164)
832. feature "COL7A1" (0.000163)
833. feature "OBSCN" (0.000163)
834. feature "PYCARD" (0.000163)
835. feature "MINDY2" (0.000163)
836. feature "RPL12P38" (0.000162)
837. feature "UBE2D1" (0.000162)
838. feature "ETAA1" (0.000162)
839. feature "CARM1" (0.000162)
840. feature "LDOC1" (0.000162)
841. feature "TOMM5" (0.000161)
842. feature "NCK1" (0.000161)
843. feature "PAPSS2" (0.000160)
844. feature "RARG" (0.000160)
845. feature "ANKMY2" (0.000160)
846. feature "BRWD3" (0.000159)
847. feature "NEDD9" (0.000159)
848. feature "TASOR" (0.000159)
849. feature "TINAGL1" (0.000158)
850. feature "NUP93" (0.000158)
851. feature "MCM6" (0.000157)
852. feature "SF3B4" (0.000157)
853. feature "UBN2" (0.000157)
854. feature "EFNB2" (0.000157)
855. feature "SPRYD4" (0.000157)
856. feature "PYCR1" (0.000156)
857. feature "KDM4B" (0.000156)
858. feature "MARVELD1" (0.000155)
859. feature "DGUOK-AS1" (0.000155)
860. feature "MLLT10" (0.000155)
861. feature "CNTRL" (0.000155)
862. feature "SPRYD3" (0.000155)
863. feature "SOX11" (0.000155)
864. feature "SCCPDH" (0.000154)
865. feature "EFCAB2" (0.000154)
866. feature "RAB5B" (0.000153)
867. feature "TTYH3" (0.000152)
868. feature "ARID4A" (0.000152)
869. feature "FKBP9" (0.000152)
870. feature "PFKL" (0.000152)
871. feature "IQCG" (0.000152)
872. feature "YEATS4" (0.000151)
873. feature "MLLT6" (0.000151)
874. feature "FLAD1" (0.000151)
875. feature "UNC45A" (0.000151)
876. feature "MT-TE" (0.000151)
877. feature "FUOM" (0.000150)
878. feature "POLDIP2" (0.000150)
879. feature "BTBD2" (0.000150)
880. feature "SLC38A10" (0.000150)
881. feature "TCF19" (0.000150)
882. feature "SDC1" (0.000149)
883. feature "ARHGAP23" (0.000149)
884. feature "CRBN" (0.000149)
885. feature "DANT1" (0.000149)
886. feature "SETBP1" (0.000149)
887. feature "MMP2" (0.000149)
888. feature "DGKZ" (0.000148)
889. feature "ADORA2B" (0.000148)
890. feature "SDHB" (0.000148)
891. feature "ACSL4" (0.000148)
892. feature "REXO1" (0.000148)
893. feature "FAM181B" (0.000148)
894. feature "IER5" (0.000148)
895. feature "ARID3A" (0.000148)
896. feature "SPA17" (0.000148)
897. feature "PSMG1" (0.000147)
898. feature "KXD1" (0.000147)
899. feature "RUSC1" (0.000147)
900. feature "IMP3" (0.000147)
901. feature "NSMF" (0.000147)
902. feature "ECH1" (0.000147)
903. feature "UBQLN4" (0.000147)
904. feature "GATD1" (0.000147)
905. feature "GALM" (0.000147)
906. feature "KLHL7" (0.000147)
907. feature "ZNF598" (0.000146)
908. feature "FASTKD3" (0.000146)
909. feature "LPAR4" (0.000146)
910. feature "LINC-PINT" (0.000146)
911. feature "TAF12" (0.000146)
912. feature "ASNSD1" (0.000146)
913. feature "ARAF" (0.000146)
914. feature "CAPG" (0.000145)
915. feature "SIRT1" (0.000145)
916. feature "HHLA3" (0.000145)
917. feature "TMEM256" (0.000145)
918. feature "SFMBT1" (0.000145)
919. feature "PCDHGA10" (0.000145)
920. feature "KRT4" (0.000145)
921. feature "SAMHD1" (0.000144)
922. feature "NRAV" (0.000144)
923. feature "SFXN3" (0.000144)
924. feature "ATF3" (0.000144)
925. feature "VAMP7" (0.000144)
926. feature "SINHCAFP3" (0.000143)
927. feature "SLC25A46" (0.000143)
928. feature "CSRP1" (0.000143)
929. feature "P2RY2" (0.000143)
930. feature "MBD3" (0.000143)
931. feature "MRPL49" (0.000143)
932. feature "IL18" (0.000143)
933. feature "COMT" (0.000143)
934. feature "TNFAIP8L1" (0.000142)
935. feature "SSNA1" (0.000142)
936. feature "ACVR2B" (0.000141)
937. feature "ZNF580" (0.000141)
938. feature "AJAP1" (0.000141)
939. feature "CCDC8" (0.000141)
940. feature "SMAD3" (0.000140)
941. feature "IRS2" (0.000140)
942. feature "KDM3A" (0.000140)
943. feature "IFT74" (0.000140)
944. feature "UNC13D" (0.000140)
945. feature "CPTP" (0.000140)
946. feature "NOP10" (0.000140)
947. feature "ARAP1" (0.000140)
948. feature "RPL17" (0.000140)
949. feature "PGM1" (0.000140)
950. feature "MAGED2" (0.000140)
951. feature "MRPL45" (0.000139)
952. feature "PLCD3" (0.000139)
953. feature "CCNG2" (0.000139)
954. feature "CTBP1" (0.000138)
955. feature "DCAF6" (0.000138)
956. feature "HMGB1P8" (0.000138)
957. feature "LAMA5" (0.000138)
958. feature "MARK1" (0.000138)
959. feature "RASSF3" (0.000137)
960. feature "TRDN" (0.000137)
961. feature "GYS1" (0.000137)
962. feature "AAGAB" (0.000137)
963. feature "PRPSAP2" (0.000137)
964. feature "UBE2E1" (0.000136)
965. feature "NEK1" (0.000136)
966. feature "LMF2" (0.000136)
967. feature "TRAFD1" (0.000136)
968. feature "FGF12" (0.000136)
969. feature "RAB2B" (0.000136)
970. feature "SHISA5" (0.000136)
971. feature "GNPAT" (0.000135)
972. feature "SOX15" (0.000135)
973. feature "PPP1R13L" (0.000135)
974. feature "DNA2" (0.000135)
975. feature "UBA3" (0.000135)
976. feature "MT-TC" (0.000135)
977. feature "BRAT1" (0.000134)
978. feature "DNPH1" (0.000134)
979. feature "KRT16" (0.000134)
980. feature "ZNF8-DT" (0.000134)
981. feature "RPLP0P2" (0.000133)
982. feature "CKB" (0.000133)
983. feature "SCPEP1" (0.000133)
984. feature "CEP63" (0.000133)
985. feature "ADM" (0.000133)
986. feature "MLH3" (0.000133)
987. feature "DLG5" (0.000132)
988. feature "TRIM7" (0.000132)
989. feature "TRIOBP" (0.000132)
990. feature "DBT" (0.000132)
991. feature "BLCAP" (0.000131)
992. feature "GMPPA" (0.000131)
993. feature "DYNC2I1" (0.000131)
994. feature "RNF187" (0.000130)
995. feature "KIRREL1" (0.000130)
996. feature "NUAK1" (0.000130)
997. feature "DKK1" (0.000130)
998. feature "VAV2" (0.000130)
999. feature "CNTROB" (0.000130)
1000. feature "TRMT1L" (0.000130)
1001. feature "COL6A3" (0.000129)
1002. feature "AXIN1" (0.000129)
1003. feature "TUBA5P" (0.000129)
1004. feature "HSPA2" (0.000129)
1005. feature "TLE3" (0.000129)
1006. feature "PLD5" (0.000129)
1007. feature "SGSM2" (0.000129)
1008. feature "CKS1B" (0.000129)
1009. feature "OXLD1" (0.000128)
1010. feature "ZNRF1" (0.000128)
1011. feature "UTP3" (0.000128)
1012. feature "COA5" (0.000128)
1013. feature "STK11" (0.000128)
1014. feature "AGAP1" (0.000128)
1015. feature "CNR2" (0.000127)
1016. feature "VSIR" (0.000127)
1017. feature "EFNA5" (0.000127)
1018. feature "TFPI2" (0.000126)
1019. feature "ZNF358" (0.000126)
1020. feature "TCF7L1" (0.000126)
1021. feature "PLEKHG3" (0.000126)
1022. feature "TONSL" (0.000126)
1023. feature "GPT2" (0.000126)
1024. feature "SNAP47" (0.000125)
1025. feature "ZNF280B" (0.000125)
1026. feature "SYK" (0.000125)
1027. feature "ZNF703" (0.000125)
1028. feature "CDK19" (0.000125)
1029. feature "IPO13" (0.000125)
1030. feature "CENPM" (0.000125)
1031. feature "MON1B" (0.000124)
1032. feature "TMEM40" (0.000124)
1033. feature "RHBDD2" (0.000124)
1034. feature "DTD1" (0.000124)
1035. feature "SORBS3" (0.000123)
1036. feature "HNRNPUL2" (0.000122)
1037. feature "S1PR2" (0.000122)
1038. feature "TTC28" (0.000122)
1039. feature "PPTC7" (0.000121)
1040. feature "NARS2" (0.000121)
1041. feature "CRYZ" (0.000121)
1042. feature "MOV10" (0.000121)
1043. feature "HILPDA" (0.000121)
1044. feature "FAM210B" (0.000121)
1045. feature "IVL" (0.000121)
1046. feature "LDB1" (0.000121)
1047. feature "CDC42BPG" (0.000121)
1048. feature "RNASET2" (0.000120)
1049. feature "MT-TS2" (0.000120)
1050. feature "ZFYVE9" (0.000120)
1051. feature "PPP2R5D" (0.000120)
1052. feature "OAZ2" (0.000119)
1053. feature "R3HCC1L" (0.000119)
1054. feature "OSTF1" (0.000119)
1055. feature "ERI1" (0.000119)
1056. feature "ULK1" (0.000119)
1057. feature "NELFB" (0.000118)
1058. feature "SCAPER" (0.000118)
1059. feature "ANKRD28" (0.000118)
1060. feature "BASP1" (0.000118)
1061. feature "KLLN" (0.000118)
1062. feature "SAT2" (0.000117)
1063. feature "CCS" (0.000117)
1064. feature "SIPA1L1" (0.000117)
1065. feature "C2orf68" (0.000117)
1066. feature "NUMBL" (0.000117)
1067. feature "PDE8A" (0.000117)
1068. feature "THEM4" (0.000117)
1069. feature "CITED2" (0.000117)
1070. feature "ZADH2" (0.000117)
1071. feature "SLC39A14" (0.000116)
1072. feature "CUEDC2" (0.000116)
1073. feature "NECAB3" (0.000116)
1074. feature "TMEM126A" (0.000116)
1075. feature "ZDHHC8" (0.000116)
1076. feature "VPS26B" (0.000116)
1077. feature "PMVK" (0.000115)
1078. feature "OGFRL1" (0.000115)
1079. feature "SLC35C2" (0.000115)
1080. feature "NCK1-DT" (0.000115)
1081. feature "RNF223" (0.000115)
1082. feature "KLC2" (0.000115)
1083. feature "WDR77" (0.000115)
1084. feature "EFNB1" (0.000115)
1085. feature "DTX4" (0.000115)
1086. feature "ATP13A2" (0.000115)
1087. feature "IFI6" (0.000115)
1088. feature "PRR5L" (0.000115)
1089. feature "ZNF317" (0.000115)
1090. feature "APEH" (0.000115)
1091. feature "ZBTB33" (0.000114)
1092. feature "SMIM30" (0.000114)
1093. feature "WBP2" (0.000114)
1094. feature "ZRANB1" (0.000114)
1095. feature "OVCH1-AS1" (0.000114)
1096. feature "GMPR2" (0.000114)
1097. feature "AK1" (0.000114)
1098. feature "TMEM70" (0.000114)
1099. feature "FHL2" (0.000113)
1100. feature "MID1IP1" (0.000113)
1101. feature "SH3RF1" (0.000113)
1102. feature "PLEKHA4" (0.000113)
1103. feature "RHOT2" (0.000113)
1104. feature "DAPK3" (0.000113)
1105. feature "FAM193A" (0.000113)
1106. feature "RAD51" (0.000112)
1107. feature "MAP3K14" (0.000112)
1108. feature "CEP120" (0.000112)
1109. feature "NEFL" (0.000112)
1110. feature "PA2G4P6" (0.000112)
1111. feature "SUCLG2" (0.000112)
1112. feature "PPM1A" (0.000112)
1113. feature "PCIF1" (0.000112)
1114. feature "TBL1X" (0.000111)
1115. feature "PDIA5" (0.000111)
1116. feature "ARMC6" (0.000111)
1117. feature "KDM6B" (0.000111)
1118. feature "TBC1D22A" (0.000111)
1119. feature "GLCCI1" (0.000110)
1120. feature "WT1-AS" (0.000110)
1121. feature "TAF1A" (0.000110)
1122. feature "GCAT" (0.000110)
1123. feature "B4GALT2" (0.000110)
1124. feature "POLE2" (0.000110)
1125. feature "PRXL2B" (0.000110)
1126. feature "IMPACT" (0.000110)
1127. feature "ARHGAP42" (0.000109)
1128. feature "SLC40A1" (0.000109)
1129. feature "DNAJB2" (0.000109)
1130. feature "EEF1A2" (0.000109)
1131. feature "FBXO48" (0.000109)
1132. feature "SMIM29" (0.000109)
1133. feature "TCP11L1" (0.000109)
1134. feature "FBXO42" (0.000109)
1135. feature "DAZAP2" (0.000109)
1136. feature "GSK3A" (0.000109)
1137. feature "BEND6" (0.000108)
1138. feature "CDK14" (0.000108)
1139. feature "SS18" (0.000108)
1140. feature "LRRC45" (0.000108)
1141. feature "ARHGEF28" (0.000108)
1142. feature "MZT1" (0.000107)
1143. feature "MAZ" (0.000107)
1144. feature "MAGI2-AS3" (0.000107)
1145. feature "CTXN1" (0.000107)
1146. feature "VAT1" (0.000107)
1147. feature "TMEM45A" (0.000107)
1148. feature "HMG20B" (0.000107)
1149. feature "BMPR2" (0.000107)
1150. feature "UBXN6" (0.000107)
1151. feature "AMOTL2" (0.000107)
1152. feature "DERA" (0.000106)
1153. feature "CRIP2" (0.000106)
1154. feature "CADM1" (0.000106)
1155. feature "MID1" (0.000106)
1156. feature "PDLIM4" (0.000106)
1157. feature "IDH3G" (0.000106)
1158. feature "MAN2B1" (0.000106)
1159. feature "SNCG" (0.000106)
1160. feature "GEN1" (0.000105)
1161. feature "WAC-AS1" (0.000105)
1162. feature "RPGR" (0.000104)
1163. feature "GRIN2D" (0.000104)
1164. feature "AP1S3" (0.000104)
1165. feature "RPL22L1" (0.000104)
1166. feature "IBA57" (0.000104)
1167. feature "HAUS5" (0.000104)
1168. feature "IQANK1" (0.000104)
1169. feature "CERS6" (0.000104)
1170. feature "GABPA" (0.000104)
1171. feature "TMEM80" (0.000104)
1172. feature "DIP2C" (0.000104)
1173. feature "POLG" (0.000103)
1174. feature "CTDSP2" (0.000103)
1175. feature "SMIM26" (0.000103)
1176. feature "TYMP" (0.000103)
1177. feature "SH3BP1" (0.000103)
1178. feature "CLEC11A" (0.000103)
1179. feature "IFRD2" (0.000103)
1180. feature "FAM83A-AS1" (0.000103)
1181. feature "OGDH" (0.000103)
1182. feature "EZH1" (0.000102)
1183. feature "LRAT" (0.000102)
1184. feature "HDAC7" (0.000102)
1185. feature "RHNO1" (0.000102)
1186. feature "CCNY" (0.000102)
1187. feature "DYNLT1" (0.000102)
1188. feature "GNB2" (0.000102)
1189. feature "CLASRP" (0.000102)
1190. feature "MUL1" (0.000102)
1191. feature "BCL2L12" (0.000102)
1192. feature "MIXL1" (0.000101)
1193. feature "CCDC92" (0.000101)
1194. feature "HSBP1L1" (0.000101)
1195. feature "PCDHB1" (0.000101)
1196. feature "CHTF18" (0.000101)
1197. feature "FYCO1" (0.000100)
1198. feature "TFE3" (0.000100)
1199. feature "PLEKHG4B" (0.000100)
1200. feature "SFTA1P" (0.000100)
1201. feature "CCDC26" (0.000100)
1202. feature "VPS52" (0.000100)
1203. feature "FBXO30" (0.000099)
1204. feature "SHMT1" (0.000099)
1205. feature "CLEC4OP" (0.000099)
1206. feature "PISD" (0.000099)
1207. feature "ALG13" (0.000099)
1208. feature "C16orf91" (0.000099)
1209. feature "PLP2" (0.000099)
1210. feature "GATA2-AS1" (0.000099)
1211. feature "GAS8" (0.000099)
1212. feature "CRYZL1" (0.000099)
1213. feature "CCDC74A" (0.000099)
1214. feature "RAVER1" (0.000099)
1215. feature "ATP5MC2" (0.000099)
1216. feature "ITM2C" (0.000099)
1217. feature "LDLRAP1" (0.000098)
1218. feature "RNPEPL1" (0.000098)
1219. feature "IGFBP7" (0.000097)
1220. feature "VAMP2" (0.000097)
1221. feature "CHMP1A" (0.000097)
1222. feature "RABGGTA" (0.000097)
1223. feature "ZNF654" (0.000097)
1224. feature "RETREG2" (0.000096)
1225. feature "PIKFYVE" (0.000096)
1226. feature "MCRIP1" (0.000096)
1227. feature "ZNF581" (0.000096)
1228. feature "XRCC1" (0.000096)
1229. feature "RTL6" (0.000096)
1230. feature "TMEM250" (0.000096)
1231. feature "ST8SIA6-AS1" (0.000096)
1232. feature "ZNF184" (0.000096)
1233. feature "POLR2H" (0.000095)
1234. feature "VPS54" (0.000095)
1235. feature "KCTD1" (0.000095)
1236. feature "C1orf115" (0.000094)
1237. feature "RNF25" (0.000094)
1238. feature "C9orf64" (0.000094)
1239. feature "NEBL" (0.000094)
1240. feature "PLIN3" (0.000094)
1241. feature "RHBDL2" (0.000094)
1242. feature "SNHG18" (0.000094)
1243. feature "PPIL1" (0.000093)
1244. feature "C15orf39" (0.000092)
1245. feature "KIF9" (0.000092)
1246. feature "ZBED2" (0.000092)
1247. feature "BICRA" (0.000092)
1248. feature "BCL7B" (0.000092)
1249. feature "TMEM25" (0.000092)
1250. feature "JMJD6" (0.000092)
1251. feature "HIP1R" (0.000092)
1252. feature "SMIM1" (0.000092)
1253. feature "WDR90" (0.000092)
1254. feature "ARHGEF39" (0.000091)
1255. feature "WDR74" (0.000091)
1256. feature "TBC1D16" (0.000091)
1257. feature "NOA1" (0.000091)
1258. feature "CASP6" (0.000091)
1259. feature "RAB8B" (0.000091)
1260. feature "RPA1" (0.000091)
1261. feature "TFIP11" (0.000091)
1262. feature "SURF1" (0.000090)
1263. feature "HEY1" (0.000090)
1264. feature "GIN1" (0.000090)
1265. feature "SUN2" (0.000090)
1266. feature "ADARB1" (0.000090)
1267. feature "SPP1" (0.000090)
1268. feature "FASTKD5" (0.000090)
1269. feature "CACTIN" (0.000090)
1270. feature "TXNRD2" (0.000090)
1271. feature "DLX6" (0.000089)
1272. feature "CRNDE" (0.000089)
1273. feature "LINC01116" (0.000089)
1274. feature "CASP3" (0.000089)
1275. feature "FASTK" (0.000089)
1276. feature "CDCA4" (0.000089)
1277. feature "KRT15" (0.000089)
1278. feature "PHLPP1" (0.000088)
1279. feature "YY1AP1" (0.000088)
1280. feature "EPHX1" (0.000088)
1281. feature "MREG" (0.000088)
1282. feature "DPRX" (0.000088)
1283. feature "BACE2" (0.000088)
1284. feature "NEK6" (0.000088)
1285. feature "MICAL2" (0.000088)
1286. feature "SH2B1" (0.000088)
1287. feature "AAMDC" (0.000088)
1288. feature "SNORD3B-1" (0.000087)
1289. feature "MT-TL1" (0.000087)
1290. feature "PHEX" (0.000087)
1291. feature "MIR200CHG" (0.000087)
1292. feature "FAM83G" (0.000087)
1293. feature "ARHGEF18" (0.000087)
1294. feature "XPNPEP1" (0.000087)
1295. feature "KHK" (0.000087)
1296. feature "SRGAP2" (0.000087)
1297. feature "DCAKD" (0.000087)
1298. feature "TRAK1" (0.000086)
1299. feature "PHACTR1" (0.000086)
1300. feature "FOXJ2" (0.000086)
1301. feature "ZDHHC4" (0.000086)
1302. feature "BAHD1" (0.000086)
1303. feature "SLC25A28" (0.000086)
1304. feature "ZBTB2" (0.000086)
1305. feature "CYHR1" (0.000086)
1306. feature "LCA5" (0.000086)
1307. feature "CRYBB2" (0.000085)
1308. feature "PLEKHF1" (0.000085)
1309. feature "CXXC5" (0.000085)
1310. feature "FANCE" (0.000085)
1311. feature "AKAP5" (0.000085)
1312. feature "DVL1" (0.000085)
1313. feature "BORCS6" (0.000084)
1314. feature "HEBP1" (0.000084)
1315. feature "LINC01151" (0.000084)
1316. feature "TEX30" (0.000084)
1317. feature "MBD1" (0.000083)
1318. feature "C19orf47" (0.000083)
1319. feature "APBA2" (0.000083)
1320. feature "TATDN2" (0.000083)
1321. feature "TNFRSF10B" (0.000083)
1322. feature "TSEN54" (0.000083)
1323. feature "FAM120C" (0.000083)
1324. feature "RPL21P10" (0.000082)
1325. feature "IPMK" (0.000082)
1326. feature "AP1S2" (0.000082)
1327. feature "ADAP1" (0.000082)
1328. feature "AFAP1L2" (0.000082)
1329. feature "RSPH3" (0.000082)
1330. feature "ZNF22" (0.000082)
1331. feature "RHOV" (0.000082)
1332. feature "UNC119B" (0.000082)
1333. feature "AIP" (0.000081)
1334. feature "SNHG19" (0.000081)
1335. feature "E2F1" (0.000081)
1336. feature "FNDC10" (0.000081)
1337. feature "NECTIN2" (0.000081)
1338. feature "USP32" (0.000081)
1339. feature "ERG28" (0.000081)
1340. feature "TBC1D7" (0.000081)
1341. feature "CLIP2" (0.000081)
1342. feature "SRSF8" (0.000081)
1343. feature "ELF4" (0.000080)
1344. feature "AMZ2P1" (0.000080)
1345. feature "FBXL19" (0.000080)
1346. feature "GALNS" (0.000080)
1347. feature "PDPK1" (0.000080)
1348. feature "SLC39A11" (0.000080)
1349. feature "B3GALT6" (0.000080)
1350. feature "ARMH3" (0.000080)
1351. feature "BET1" (0.000079)
1352. feature "PRR15" (0.000079)
1353. feature "C16orf74" (0.000079)
1354. feature "ELP2" (0.000079)
1355. feature "HIGD1A" (0.000079)
1356. feature "ANXA4" (0.000079)
1357. feature "UBQLN2" (0.000079)
1358. feature "SELENOO" (0.000079)
1359. feature "LRRIQ1" (0.000078)
1360. feature "SLC2A11" (0.000078)
1361. feature "FBXL15" (0.000078)
1362. feature "MSX1" (0.000078)
1363. feature "CCNG1" (0.000078)
1364. feature "PITPNM1" (0.000078)
1365. feature "PTPN6" (0.000078)
1366. feature "FAM189B" (0.000078)
1367. feature "ACSF3" (0.000078)
1368. feature "ANKRD27" (0.000078)
1369. feature "ZNF195" (0.000078)
1370. feature "STUB1" (0.000078)
1371. feature "AAAS" (0.000078)
1372. feature "OPRM1" (0.000078)
1373. feature "SP2" (0.000078)
1374. feature "ZDHHC7" (0.000077)
1375. feature "BICC1" (0.000077)
1376. feature "NTN4" (0.000077)
1377. feature "SEMA3D" (0.000077)
1378. feature "NCOA1" (0.000077)
1379. feature "TRIM8" (0.000077)
1380. feature "NKX3-1" (0.000076)
1381. feature "SBNO2" (0.000076)
1382. feature "PGBD1" (0.000076)
1383. feature "WDR6" (0.000076)
1384. feature "BCAR1" (0.000076)
1385. feature "WDR53" (0.000076)
1386. feature "OTULINL" (0.000076)
1387. feature "DOHH" (0.000075)
1388. feature "NT5C" (0.000075)
1389. feature "FABP6" (0.000075)
1390. feature "PCYT1B" (0.000075)
1391. feature "FPGS" (0.000075)
1392. feature "GM2A" (0.000075)
1393. feature "ZCWPW1" (0.000075)
1394. feature "PSMD9" (0.000075)
1395. feature "DYSF" (0.000074)
1396. feature "GAS2L1" (0.000074)
1397. feature "KDM5C" (0.000074)
1398. feature "RFK" (0.000074)
1399. feature "FAM114A2" (0.000074)
1400. feature "IL6R" (0.000074)
1401. feature "FYN" (0.000074)
1402. feature "XYLB" (0.000074)
1403. feature "FGF8" (0.000073)
1404. feature "DNAH11" (0.000073)
1405. feature "TTC3P1" (0.000073)
1406. feature "TRIM52-AS1" (0.000073)
1407. feature "MAML2" (0.000073)
1408. feature "KIF3C" (0.000073)
1409. feature "GNG4" (0.000073)
1410. feature "JAKMIP3" (0.000072)
1411. feature "PKN1" (0.000072)
1412. feature "ZNF470" (0.000072)
1413. feature "LINC00342" (0.000072)
1414. feature "FAM20C" (0.000072)
1415. feature "PTPRU" (0.000072)
1416. feature "PANX2" (0.000072)
1417. feature "CROCC" (0.000072)
1418. feature "FBXL17" (0.000072)
1419. feature "ULK2" (0.000072)
1420. feature "LHPP" (0.000072)
1421. feature "ARNTL" (0.000072)
1422. feature "MEX3D" (0.000071)
1423. feature "DHRS4" (0.000071)
1424. feature "ZNF558" (0.000071)
1425. feature "ZNF33B" (0.000071)
1426. feature "DCAF10" (0.000071)
1427. feature "ZXDC" (0.000071)
1428. feature "ANKEF1" (0.000071)
1429. feature "LINC02210" (0.000071)
1430. feature "GPR153" (0.000071)
1431. feature "IFFO2" (0.000071)
1432. feature "EGLN2" (0.000070)
1433. feature "FGFR3" (0.000070)
1434. feature "CMTM4" (0.000070)
1435. feature "CRPPA" (0.000070)
1436. feature "MT-TN" (0.000070)
1437. feature "PI4KB" (0.000070)
1438. feature "SETD1A" (0.000070)
1439. feature "DHCR7" (0.000070)
1440. feature "BIN1" (0.000069)
1441. feature "MYBL1" (0.000069)
1442. feature "TGFB1I1" (0.000069)
1443. feature "SYDE1" (0.000069)
1444. feature "THNSL1" (0.000069)
1445. feature "TMPRSS13" (0.000069)
1446. feature "TCHH" (0.000069)
1447. feature "YPEL5" (0.000069)
1448. feature "SIX3" (0.000069)
1449. feature "LINC01322" (0.000069)
1450. feature "DOCK9" (0.000068)
1451. feature "IGSF6" (0.000068)
1452. feature "FAM13B" (0.000068)
1453. feature "HAUS7" (0.000068)
1454. feature "NECAP1" (0.000068)
1455. feature "C11orf49" (0.000068)
1456. feature "VDR" (0.000068)
1457. feature "PLK4" (0.000068)
1458. feature "TMEM39B" (0.000068)
1459. feature "ARSA" (0.000068)
1460. feature "HELQ" (0.000068)
1461. feature "ZNF165" (0.000068)
1462. feature "TRAPPC2B" (0.000068)
1463. feature "R3HCC1" (0.000067)
1464. feature "PRECSIT" (0.000067)
1465. feature "CDR2" (0.000067)
1466. feature "PGPEP1" (0.000067)
1467. feature "RCHY1" (0.000067)
1468. feature "PLCG1" (0.000067)
1469. feature "KCTD2" (0.000067)
1470. feature "ABHD17A" (0.000067)
1471. feature "SIX2" (0.000067)
1472. feature "C1QTNF6" (0.000067)
1473. feature "SNX22" (0.000066)
1474. feature "RAD9A" (0.000066)
1475. feature "ESR1" (0.000066)
1476. feature "ABHD10" (0.000066)
1477. feature "NTAN1" (0.000066)
1478. feature "HPRT1" (0.000066)
1479. feature "LTBP3" (0.000066)
1480. feature "RTL8C" (0.000066)
1481. feature "PXMP4" (0.000066)
1482. feature "ZNF718" (0.000066)
1483. feature "WARS2" (0.000066)
1484. feature "TEAD3" (0.000066)
1485. feature "COPZ2" (0.000066)
1486. feature "MFSD5" (0.000065)
1487. feature "HAS3" (0.000065)
1488. feature "MAP3K6" (0.000065)
1489. feature "STYX" (0.000065)
1490. feature "MYPOP" (0.000065)
1491. feature "RAB20" (0.000065)
1492. feature "SPATC1L" (0.000065)
1493. feature "SECISBP2L" (0.000065)
1494. feature "ABT1" (0.000065)
1495. feature "GPRC5D-AS1" (0.000065)
1496. feature "C19orf54" (0.000065)
1497. feature "DBF4B" (0.000064)
1498. feature "SDHAF3" (0.000064)
1499. feature "NDST1" (0.000064)
1500. feature "ALDH3B1" (0.000064)
1501. feature "MT-TA" (0.000064)
1502. feature "NAALADL2" (0.000064)
1503. feature "UBL4A" (0.000064)
1504. feature "PTK2B" (0.000064)
1505. feature "PYGO1" (0.000064)
1506. feature "DECR2" (0.000064)
1507. feature "GPM6B" (0.000063)
1508. feature "RNF19B" (0.000063)
1509. feature "SELENON" (0.000063)
1510. feature "POLR3GL" (0.000063)
1511. feature "UBALD2" (0.000062)
1512. feature "PID1" (0.000062)
1513. feature "NAIP" (0.000062)
1514. feature "GSDME" (0.000062)
1515. feature "SESN2" (0.000062)
1516. feature "PLAC8" (0.000062)
1517. feature "MAP1A" (0.000062)
1518. feature "MAPK8" (0.000062)
1519. feature "MTO1" (0.000062)
1520. feature "BTBD7P1" (0.000062)
1521. feature "ZNF628" (0.000062)
1522. feature "PATZ1" (0.000062)
1523. feature "SLC25A53" (0.000062)
1524. feature "CTTN-DT" (0.000062)
1525. feature "ILRUN-AS1" (0.000062)
1526. feature "MAPK8IP3" (0.000061)
1527. feature "SPN" (0.000061)
1528. feature "PPP1R14C" (0.000061)
1529. feature "INKA1" (0.000061)
1530. feature "MAPK12" (0.000060)
1531. feature "NAXD" (0.000060)
1532. feature "C12orf60" (0.000060)
1533. feature "PRKCD" (0.000060)
1534. feature "NCK2" (0.000060)
1535. feature "WWC1" (0.000060)
1536. feature "ZBTB34" (0.000060)
1537. feature "SIRT5" (0.000060)
1538. feature "ZNF850" (0.000060)
1539. feature "TIMM29" (0.000060)
1540. feature "STARD4" (0.000060)
1541. feature "BIN3" (0.000060)
1542. feature "CHN1" (0.000059)
1543. feature "LINC02484" (0.000059)
1544. feature "STAT2" (0.000059)
1545. feature "PEX11A" (0.000059)
1546. feature "RAP1GAP2" (0.000059)
1547. feature "HK2" (0.000059)
1548. feature "CASP1" (0.000059)
1549. feature "PRKCH" (0.000059)
1550. feature "NEU1" (0.000059)
1551. feature "CDK5" (0.000059)
1552. feature "AKT1" (0.000059)
1553. feature "DTX2" (0.000059)
1554. feature "B3GLCT" (0.000059)
1555. feature "UMAD1" (0.000059)
1556. feature "FRG1JP" (0.000058)
1557. feature "LRRC8B" (0.000058)
1558. feature "LPP-AS2" (0.000058)
1559. feature "POLR2J3" (0.000058)
1560. feature "OBSL1" (0.000058)
1561. feature "TJAP1" (0.000058)
1562. feature "FAM107A" (0.000058)
1563. feature "CHMP7" (0.000058)
1564. feature "TRMT2A" (0.000058)
1565. feature "LINC02428" (0.000057)
1566. feature "RAB36" (0.000057)
1567. feature "NAA40" (0.000057)
1568. feature "PRELID2" (0.000057)
1569. feature "UBTD1" (0.000057)
1570. feature "TXNDC11" (0.000057)
1571. feature "TRIM21" (0.000057)
1572. feature "SPINK5" (0.000057)
1573. feature "SLC35B3" (0.000057)
1574. feature "DYRK4" (0.000057)
1575. feature "EPB41L4B" (0.000057)
1576. feature "CARD16" (0.000057)
1577. feature "KLC3" (0.000057)
1578. feature "KIFC3" (0.000057)
1579. feature "SMARCAL1" (0.000056)
1580. feature "ATP6V1B2" (0.000056)
1581. feature "LINC01186" (0.000056)
1582. feature "EML1" (0.000056)
1583. feature "EIF3J-DT" (0.000056)
1584. feature "PER3" (0.000056)
1585. feature "OXNAD1" (0.000056)
1586. feature "MANBA" (0.000056)
1587. feature "ZNF524" (0.000056)
1588. feature "RPL21P34" (0.000056)
1589. feature "UGT1A7" (0.000056)
1590. feature "GMEB2" (0.000056)
1591. feature "VPS9D1" (0.000056)
1592. feature "ARHGAP22" (0.000055)
1593. feature "ZFYVE27" (0.000055)
1594. feature "JADE1" (0.000055)
1595. feature "ZNF250" (0.000055)
1596. feature "THAP4" (0.000055)
1597. feature "CCDC136" (0.000055)
1598. feature "POLA2" (0.000055)
1599. feature "LYSMD1" (0.000055)
1600. feature "INPP5E" (0.000055)
1601. feature "ZDHHC18" (0.000055)
1602. feature "MAP3K12" (0.000055)
1603. feature "SENP3" (0.000055)
1604. feature "PORCN" (0.000055)
1605. feature "RAB40C" (0.000055)
1606. feature "TWIST1" (0.000055)
1607. feature "DELE1" (0.000055)
1608. feature "MRTFA" (0.000055)
1609. feature "SELENOP" (0.000054)
1610. feature "KCTD15" (0.000054)
1611. feature "ZBTB10" (0.000054)
1612. feature "DOP1A" (0.000054)
1613. feature "PALM3" (0.000054)
1614. feature "MED18" (0.000054)
1615. feature "MTFP1" (0.000054)
1616. feature "C3orf14" (0.000054)
1617. feature "WDR27" (0.000054)
1618. feature "MRPS36" (0.000054)
1619. feature "RPL12P13" (0.000054)
1620. feature "PANX1" (0.000054)
1621. feature "BOLA3-AS1" (0.000054)
1622. feature "ATP6V0C" (0.000054)
1623. feature "HNF4A" (0.000053)
1624. feature "KLHDC3" (0.000053)
1625. feature "HFE" (0.000053)
1626. feature "S1PR3" (0.000053)
1627. feature "FSD1L" (0.000053)
1628. feature "C16orf95-DT" (0.000053)
1629. feature "AGO4" (0.000053)
1630. feature "RBP1" (0.000053)
1631. feature "ZNF880" (0.000053)
1632. feature "CRLF1" (0.000053)
1633. feature "BRSK2" (0.000053)
1634. feature "LINC02870" (0.000053)
1635. feature "SHANK2" (0.000053)
1636. feature "ATP5PDP3" (0.000053)
1637. feature "ZNF251" (0.000053)
1638. feature "MMP25-AS1" (0.000053)
1639. feature "TMEM44-AS1" (0.000053)
1640. feature "RAB11FIP5" (0.000053)
1641. feature "ZNF516" (0.000052)
1642. feature "NAT14" (0.000052)
1643. feature "WASF3" (0.000052)
1644. feature "ZNF777" (0.000052)
1645. feature "PLEKHO1" (0.000052)
1646. feature "EPB41L4A-AS1" (0.000052)
1647. feature "CPNE2" (0.000052)
1648. feature "FUT11" (0.000052)
1649. feature "FBXL3" (0.000052)
1650. feature "GLB1" (0.000052)
1651. feature "ZBTB26" (0.000052)
1652. feature "RNASEH1-AS1" (0.000052)
1653. feature "DTWD2" (0.000052)
1654. feature "NRXN3" (0.000052)
1655. feature "CTDP1" (0.000052)
1656. feature "TEDC2" (0.000052)
1657. feature "RBM43" (0.000052)
1658. feature "CACNB3" (0.000051)
1659. feature "PTPDC1" (0.000051)
1660. feature "FKTN" (0.000051)
1661. feature "MTND4P12" (0.000051)
1662. feature "MAP3K1" (0.000051)
1663. feature "OPN3" (0.000051)
1664. feature "TAB1" (0.000051)
1665. feature "DPY19L1" (0.000051)
1666. feature "ACYP2" (0.000051)
1667. feature "PER1" (0.000051)
1668. feature "LINC01962" (0.000051)
1669. feature "FOXN3-AS1" (0.000051)
1670. feature "EIPR1" (0.000051)
1671. feature "PIK3R2" (0.000051)
1672. feature "INTS14" (0.000051)
1673. feature "ERP27" (0.000051)
1674. feature "ALS2CL" (0.000051)
1675. feature "FBXO38" (0.000050)
1676. feature "MACROH2A2" (0.000050)
1677. feature "CAMKK1" (0.000050)
1678. feature "NSMCE3" (0.000050)
1679. feature "NRARP" (0.000050)
1680. feature "RHBDF1" (0.000050)
1681. feature "TMEM52" (0.000050)
1682. feature "DGLUCY" (0.000050)
1683. feature "DMPK" (0.000050)
1684. feature "GJB3" (0.000050)
1685. feature "THAP2" (0.000050)
1686. feature "HAAO" (0.000050)
1687. feature "SAP30L-AS1" (0.000050)
1688. feature "C18orf32" (0.000050)
1689. feature "PIM3" (0.000050)
1690. feature "POLD1" (0.000050)
1691. feature "ZNF629" (0.000050)
1692. feature "CAMSAP3" (0.000050)
1693. feature "FAM228B" (0.000049)
1694. feature "GKAP1" (0.000049)
1695. feature "TKFC" (0.000049)
1696. feature "TPRA1" (0.000049)
1697. feature "CDH8" (0.000049)
1698. feature "RHPN1" (0.000049)
1699. feature "MAFK" (0.000049)
1700. feature "CAPN10" (0.000049)
1701. feature "MIR210HG" (0.000049)
1702. feature "BTG2" (0.000048)
1703. feature "SAP30L" (0.000048)
1704. feature "LATS2" (0.000048)
1705. feature "SQOR" (0.000048)
1706. feature "MELTF-AS1" (0.000048)
1707. feature "RUNX2" (0.000048)
1708. feature "RASSF7" (0.000048)
1709. feature "HMGN1P26" (0.000048)
1710. feature "PROSER2" (0.000048)
1711. feature "SLC4A2" (0.000048)
1712. feature "TIGD5" (0.000048)
1713. feature "TCF15" (0.000048)
1714. feature "CELSR1" (0.000048)
1715. feature "ORMDL3" (0.000048)
1716. feature "RGL2" (0.000048)
1717. feature "ZCCHC3" (0.000048)
1718. feature "NFKBIL1" (0.000048)
1719. feature "TSGA10" (0.000048)
1720. feature "PRIMPOL" (0.000047)
1721. feature "PLEKHA8P1" (0.000047)
1722. feature "LINC02076" (0.000047)
1723. feature "UVSSA" (0.000047)
1724. feature "CCDC71L" (0.000047)
1725. feature "RBBP8NL" (0.000047)
1726. feature "SAP30-DT" (0.000047)
1727. feature "PIH1D2" (0.000047)
1728. feature "NLE1" (0.000047)
1729. feature "RTL8A" (0.000047)
1730. feature "KBTBD3" (0.000047)
1731. feature "NUDT8" (0.000047)
1732. feature "CPE" (0.000047)
1733. feature "ZBTB12" (0.000047)
1734. feature "NIF3L1" (0.000047)
1735. feature "FAM171B" (0.000047)
1736. feature "PKN3" (0.000046)
1737. feature "SLC25A22" (0.000046)
1738. feature "MTCO1P12" (0.000046)
1739. feature "KCP" (0.000046)
1740. feature "FANCF" (0.000046)
1741. feature "UNK" (0.000046)
1742. feature "TRIB2" (0.000046)
1743. feature "HAP1" (0.000046)
1744. feature "RTN4R" (0.000046)
1745. feature "DPYSL5" (0.000046)
1746. feature "CCDC102A" (0.000046)
1747. feature "HOXA11-AS" (0.000046)
1748. feature "S100A4" (0.000046)
1749. feature "SMTN" (0.000046)
1750. feature "GAS6-DT" (0.000046)
1751. feature "MED23" (0.000046)
1752. feature "MXD3" (0.000046)
1753. feature "NIPSNAP3A" (0.000046)
1754. feature "ZNF117" (0.000045)
1755. feature "VPS16" (0.000045)
1756. feature "MFAP2" (0.000045)
1757. feature "DTD2" (0.000045)
1758. feature "CHMP6" (0.000045)
1759. feature "MOCOS" (0.000045)
1760. feature "COL27A1" (0.000045)
1761. feature "SAMD15" (0.000045)
1762. feature "HOXC13" (0.000045)
1763. feature "MIB2" (0.000045)
1764. feature "HES2" (0.000045)
1765. feature "CNFN" (0.000045)
1766. feature "MAN1B1-DT" (0.000045)
1767. feature "IFNGR2" (0.000045)
1768. feature "TMEM94" (0.000045)
1769. feature "ERG" (0.000045)
1770. feature "SYVN1" (0.000045)
1771. feature "SLC16A4" (0.000045)
1772. feature "HLA-F" (0.000044)
1773. feature "CST6" (0.000044)
1774. feature "TRIM39" (0.000044)
1775. feature "SSPN" (0.000044)
1776. feature "DUSP5" (0.000044)
1777. feature "GSEC" (0.000044)
1778. feature "BDKRB2" (0.000044)
1779. feature "KIF26A" (0.000044)
1780. feature "MFSD13A" (0.000044)
1781. feature "PIGC" (0.000044)
1782. feature "SMG8" (0.000044)
1783. feature "FOXD2" (0.000044)
1784. feature "ZFP30" (0.000044)
1785. feature "PTRHD1" (0.000044)
1786. feature "BAIAP2-DT" (0.000044)
1787. feature "RALGDS" (0.000044)
1788. feature "E4F1" (0.000044)
1789. feature "PODXL2" (0.000043)
1790. feature "ZNF446" (0.000043)
1791. feature "EAF2" (0.000043)
1792. feature "TMEM184B" (0.000043)
1793. feature "PTPN21" (0.000043)
1794. feature "MCEE" (0.000043)
1795. feature "RNF170" (0.000043)
1796. feature "CCDC88B" (0.000043)
1797. feature "COL5A1" (0.000043)
1798. feature "PAX5" (0.000043)
1799. feature "KAT14" (0.000043)
1800. feature "MTATP6P1" (0.000043)
1801. feature "MSL3P1" (0.000043)
1802. feature "KLHL22" (0.000043)
1803. feature "PIGK" (0.000043)
1804. feature "OR7E14P" (0.000043)
1805. feature "ZKSCAN2" (0.000043)
1806. feature "LRRC37A3" (0.000043)
1807. feature "MCM3AP-AS1" (0.000042)
1808. feature "MVK" (0.000042)
1809. feature "PLD2" (0.000042)
1810. feature "PSMG3-AS1" (0.000042)
1811. feature "ADA" (0.000042)
1812. feature "P3H1" (0.000042)
1813. feature "TAFAZZIN" (0.000042)
1814. feature "SMIM20" (0.000042)
1815. feature "DPPA2" (0.000042)
1816. feature "TRAF4" (0.000042)
1817. feature "TROAP" (0.000042)
1818. feature "KHNYN" (0.000042)
1819. feature "PARP10" (0.000042)
1820. feature "NLRX1" (0.000042)
1821. feature "RRAGA" (0.000042)
1822. feature "CFD" (0.000042)
1823. feature "TAGAP-AS1" (0.000042)
1824. feature "CDK9" (0.000042)
1825. feature "ALDOC" (0.000042)
1826. feature "KRT3" (0.000042)
1827. feature "FAH" (0.000042)
1828. feature "CXCL8" (0.000042)
1829. feature "ANO7" (0.000042)
1830. feature "TEX9" (0.000042)
1831. feature "DENND3-AS1" (0.000042)
1832. feature "TBC1D13" (0.000042)
1833. feature "SIAH1" (0.000041)
1834. feature "ITGB8-AS1" (0.000041)
1835. feature "TMEM143" (0.000041)
1836. feature "ZG16B" (0.000041)
1837. feature "B4GALT7" (0.000041)
1838. feature "GLMP" (0.000041)
1839. feature "SUPT3H" (0.000041)
1840. feature "OCRL" (0.000041)
1841. feature "HMBOX1" (0.000041)
1842. feature "KCNG1" (0.000041)
1843. feature "ABCA2" (0.000041)
1844. feature "C1orf53" (0.000041)
1845. feature "FAM169A" (0.000041)
1846. feature "HEY2" (0.000041)
1847. feature "ULBP1" (0.000041)
1848. feature "UCKL1" (0.000041)
1849. feature "TEF" (0.000041)
1850. feature "LOXL2" (0.000041)
1851. feature "ZNF865" (0.000041)
1852. feature "GNAS-AS1" (0.000040)
1853. feature "RASGEF1A" (0.000040)
1854. feature "NRGN" (0.000040)
1855. feature "ADNP-AS1" (0.000040)
1856. feature "NRSN2-AS1" (0.000040)
1857. feature "TANGO2" (0.000040)
1858. feature "ZYG11A" (0.000040)
1859. feature "HOXC13-AS" (0.000040)
1860. feature "ZNF407-AS1" (0.000040)
1861. feature "RECQL5" (0.000040)
1862. feature "CRLS1" (0.000040)
1863. feature "PCED1A" (0.000040)
1864. feature "C6orf136" (0.000040)
1865. feature "EVI5L" (0.000040)
1866. feature "EXOSC10-AS1" (0.000040)
1867. feature "TRPM2" (0.000040)
1868. feature "MMAA" (0.000040)
1869. feature "COMMD10" (0.000040)
1870. feature "HYAL3" (0.000040)
1871. feature "PCDHGC3" (0.000039)
1872. feature "SH3D21" (0.000039)
1873. feature "JPH3" (0.000039)
1874. feature "GABRE" (0.000039)
1875. feature "CEACAM19" (0.000039)
1876. feature "FBXO4" (0.000039)
1877. feature "AFAP1L1" (0.000039)
1878. feature "ZNF710" (0.000039)
1879. feature "RAPGEF3" (0.000039)
1880. feature "ZFP28" (0.000039)
1881. feature "TSHZ2" (0.000039)
1882. feature "PKD1L1" (0.000039)
1883. feature "ARL9" (0.000038)
1884. feature "ZNF319" (0.000038)
1885. feature "PKMYT1" (0.000038)
1886. feature "TMEM121" (0.000038)
1887. feature "SLC26A6" (0.000038)
1888. feature "B4GAT1" (0.000038)
1889. feature "HLA-DPB1" (0.000038)
1890. feature "MOGS" (0.000038)
1891. feature "NAB2" (0.000038)
1892. feature "USP35" (0.000038)
1893. feature "MEX3C" (0.000038)
1894. feature "ALPK1" (0.000038)
1895. feature "LINC02225" (0.000038)
1896. feature "MAGEA12" (0.000038)
1897. feature "DOCK6" (0.000038)
1898. feature "ZNF395" (0.000038)
1899. feature "REPS2" (0.000038)
1900. feature "ZNF784" (0.000038)
1901. feature "IL4R" (0.000038)
1902. feature "SYT7" (0.000038)
1903. feature "FBXO2" (0.000038)
1904. feature "SNX18" (0.000037)
1905. feature "TP73-AS1" (0.000037)
1906. feature "C1orf112" (0.000037)
1907. feature "ISOC1" (0.000037)
1908. feature "SLC22A5" (0.000037)
1909. feature "IER5L" (0.000037)
1910. feature "NEK3" (0.000037)
1911. feature "PXDN" (0.000037)
1912. feature "PLEKHA7" (0.000037)
1913. feature "IL12B" (0.000037)
1914. feature "IL17RC" (0.000037)
1915. feature "DEPDC1B" (0.000037)
1916. feature "LINC01559" (0.000037)
1917. feature "CALCOCO1" (0.000037)
1918. feature "ZNF335" (0.000037)
1919. feature "TIGD1" (0.000037)
1920. feature "PDE6D" (0.000037)
1921. feature "LINC02080" (0.000037)
1922. feature "PBXIP1" (0.000037)
1923. feature "LINC00960" (0.000037)
1924. feature "PKIB" (0.000037)
1925. feature "SMIM4" (0.000037)
1926. feature "ANKRA2" (0.000037)
1927. feature "IFIT1" (0.000036)
1928. feature "TBCCD1" (0.000036)
1929. feature "KRT18P59" (0.000036)
1930. feature "NRG4" (0.000036)
1931. feature "TMTC2" (0.000036)
1932. feature "PEX11B" (0.000036)
1933. feature "CCN2" (0.000036)
1934. feature "SREBF1" (0.000036)
1935. feature "EFNA1" (0.000036)
1936. feature "VPS51" (0.000036)
1937. feature "GLI4" (0.000036)
1938. feature "SLC9A8" (0.000036)
1939. feature "PPP1R3G" (0.000036)
1940. feature "CDPF1" (0.000036)
1941. feature "BEX5" (0.000036)
1942. feature "SH3BGRL2" (0.000036)
1943. feature "GJD2" (0.000036)
1944. feature "OAZ3" (0.000036)
1945. feature "NDOR1" (0.000036)
1946. feature "C10orf88" (0.000036)
1947. feature "CRYM-AS1" (0.000036)
1948. feature "SDAD1P1" (0.000036)
1949. feature "ZNF875" (0.000035)
1950. feature "MTLN" (0.000035)
1951. feature "SHFL" (0.000035)
1952. feature "TLL1" (0.000035)
1953. feature "UCK1" (0.000035)
1954. feature "DOP1B" (0.000035)
1955. feature "PIM1" (0.000035)
1956. feature "NPL" (0.000035)
1957. feature "PTCH1" (0.000035)
1958. feature "ASB7" (0.000035)
1959. feature "OR5M2P" (0.000035)
1960. feature "ZNF132" (0.000035)
1961. feature "FAM234B" (0.000035)
1962. feature "LRFN4" (0.000035)
1963. feature "TMEM179B" (0.000035)
1964. feature "ZNF74" (0.000035)
1965. feature "NEU3" (0.000034)
1966. feature "CHIC2" (0.000034)
1967. feature "TRPV4" (0.000034)
1968. feature "PIGBOS1" (0.000034)
1969. feature "MAGEA3" (0.000034)
1970. feature "CCDC51" (0.000034)
1971. feature "GORASP1" (0.000034)
1972. feature "ZFP3" (0.000034)
1973. feature "ELFN2" (0.000034)
1974. feature "RHOB" (0.000034)
1975. feature "CNKSR1" (0.000034)
1976. feature "TXNRD3" (0.000034)
1977. feature "ZNF542P" (0.000034)
1978. feature "HLA-G" (0.000034)
1979. feature "DLK2" (0.000034)
1980. feature "SWSAP1" (0.000034)
1981. feature "DARS1-AS1" (0.000034)
1982. feature "KDM4C" (0.000034)
1983. feature "ZNF23" (0.000034)
1984. feature "FMC1" (0.000034)
1985. feature "DLX2" (0.000034)
1986. feature "DUSP12" (0.000033)
1987. feature "PRDM11" (0.000033)
1988. feature "KHDC1" (0.000033)
1989. feature "ARL10" (0.000033)
1990. feature "SLC29A2" (0.000033)
1991. feature "NINL" (0.000033)
1992. feature "BTBD6" (0.000033)
1993. feature "SLC30A2" (0.000033)
1994. feature "AKR1B10" (0.000033)
1995. feature "SSH3" (0.000033)
1996. feature "ABHD14A" (0.000033)
1997. feature "SLC52A1" (0.000033)
1998. feature "ADGRL3" (0.000033)
1999. feature "SREBF2-AS1" (0.000033)
2000. feature "DNHD1" (0.000033)
2001. feature "PIP4P2" (0.000033)
2002. feature "RAB11B-AS1" (0.000033)
2003. feature "DNAAF9" (0.000033)
2004. feature "ABAT" (0.000033)
2005. feature "PLCD1" (0.000033)
2006. feature "IMMP2L" (0.000033)
2007. feature "TMEM8B" (0.000033)
2008. feature "TSPAN15" (0.000033)
2009. feature "TRIM35" (0.000033)
2010. feature "CCSER1" (0.000033)
2011. feature "D2HGDH" (0.000033)
2012. feature "PRR14" (0.000033)
2013. feature "EXOC3-AS1" (0.000033)
2014. feature "LRATD1" (0.000033)
2015. feature "BMP1" (0.000033)
2016. feature "SLC24A2" (0.000033)
2017. feature "BAMBI" (0.000033)
2018. feature "B3GALT1" (0.000033)
2019. feature "MAPK7" (0.000032)
2020. feature "ZC3H10" (0.000032)
2021. feature "DIPK1A" (0.000032)
2022. feature "ZNF804A" (0.000032)
2023. feature "MYADM" (0.000032)
2024. feature "TESMIN" (0.000032)
2025. feature "TP53I13" (0.000032)
2026. feature "FBXO32" (0.000032)
2027. feature "ITPKC" (0.000032)
2028. feature "PPCDC" (0.000032)
2029. feature "FKBP9P1" (0.000032)
2030. feature "NRF1" (0.000032)
2031. feature "DNLZ" (0.000032)
2032. feature "CRACR2A" (0.000032)
2033. feature "PBX3" (0.000032)
2034. feature "HECA" (0.000032)
2035. feature "CACHD1" (0.000032)
2036. feature "SYTL4" (0.000032)
2037. feature "NMRK1" (0.000032)
2038. feature "GPRC5C" (0.000032)
2039. feature "TUBBP5" (0.000032)
2040. feature "HNRNPCP3" (0.000032)
2041. feature "ARMC2" (0.000032)
2042. feature "NCKIPSD" (0.000032)
2043. feature "DENND4A" (0.000032)
2044. feature "SOX21-AS1" (0.000032)
2045. feature "ITGB6" (0.000032)
2046. feature "DXO" (0.000031)
2047. feature "VGLL1" (0.000031)
2048. feature "MAML1" (0.000031)
2049. feature "NTRK3" (0.000031)
2050. feature "TIAM2" (0.000031)
2051. feature "DIXDC1" (0.000031)
2052. feature "PWRN1" (0.000031)
2053. feature "HERC6" (0.000031)
2054. feature "TEP1" (0.000031)
2055. feature "GBA" (0.000031)
2056. feature "KCTD14" (0.000031)
2057. feature "VPS45" (0.000031)
2058. feature "ZNF514" (0.000031)
2059. feature "MYL9" (0.000031)
2060. feature "DUSP1" (0.000031)
2061. feature "METTL25B" (0.000031)
2062. feature "CSRNP1" (0.000031)
2063. feature "LTBP4" (0.000031)
2064. feature "FHDC1" (0.000031)
2065. feature "TXNDC15" (0.000031)
2066. feature "RNF185" (0.000031)
2067. feature "AMPD2" (0.000030)
2068. feature "PLXND1" (0.000030)
2069. feature "ARVCF" (0.000030)
2070. feature "SUMF1" (0.000030)
2071. feature "ZNF213" (0.000030)
2072. feature "SETMAR" (0.000030)
2073. feature "B9D2" (0.000030)
2074. feature "ZNF202" (0.000030)
2075. feature "C20orf96" (0.000030)
2076. feature "CA9" (0.000030)
2077. feature "BICDL2" (0.000030)
2078. feature "ZNF45" (0.000030)
2079. feature "LINC01931" (0.000030)
2080. feature "FAM193B" (0.000030)
2081. feature "MYOSLID" (0.000030)
2082. feature "JAZF1" (0.000030)
2083. feature "MAPK8IP2" (0.000030)
2084. feature "LINC02609" (0.000030)
2085. feature "MED26" (0.000030)
2086. feature "ROM1" (0.000030)
2087. feature "KIF27" (0.000030)
2088. feature "MRPL10" (0.000030)
2089. feature "PSG2" (0.000030)
2090. feature "NRBP2" (0.000030)
2091. feature "LYPD1" (0.000030)
2092. feature "PI4K2B" (0.000030)
2093. feature "FBLN2" (0.000029)
2094. feature "IQCC" (0.000029)
2095. feature "BOLA1" (0.000029)
2096. feature "SLC27A1" (0.000029)
2097. feature "ESYT3" (0.000029)
2098. feature "CCDC168" (0.000029)
2099. feature "RNLS" (0.000029)
2100. feature "DLG4" (0.000029)
2101. feature "AGK" (0.000029)
2102. feature "KIFC2" (0.000029)
2103. feature "KBTBD7" (0.000029)
2104. feature "MAP3K9-DT" (0.000029)
2105. feature "LINC02057" (0.000029)
2106. feature "LY6G6C" (0.000029)
2107. feature "OR3A3" (0.000029)
2108. feature "CENPBD1" (0.000029)
2109. feature "EGFLAM" (0.000029)
2110. feature "ARL17B" (0.000029)
2111. feature "TRAPPC14" (0.000029)
2112. feature "KLF16" (0.000029)
2113. feature "DSCR8" (0.000029)
2114. feature "ZNF836" (0.000029)
2115. feature "IRF2-DT" (0.000029)
2116. feature "FBF1" (0.000029)
2117. feature "BCAS3" (0.000029)
2118. feature "FGD2" (0.000029)
2119. feature "PITX1-AS1" (0.000029)
2120. feature "GRB14" (0.000029)
2121. feature "KCTD11" (0.000029)
2122. feature "WHAMMP3" (0.000029)
2123. feature "TNK2" (0.000029)
2124. feature "KLHL6" (0.000029)
2125. feature "NCAM2" (0.000029)
2126. feature "NPRL2" (0.000028)
2127. feature "HSF2BP" (0.000028)
2128. feature "PNPLA3" (0.000028)
2129. feature "SH3RF2" (0.000028)
2130. feature "OVOL1" (0.000028)
2131. feature "SERPINB7" (0.000028)
2132. feature "YPEL3" (0.000028)
2133. feature "ARRDC1" (0.000028)
2134. feature "MIATNB" (0.000028)
2135. feature "ALDH5A1" (0.000028)
2136. feature "RASSF10" (0.000028)
2137. feature "ANKRD9" (0.000028)
2138. feature "RPL13AP7" (0.000028)
2139. feature "MTUS2" (0.000028)
2140. feature "TDRKH-AS1" (0.000028)
2141. feature "SH2B3" (0.000028)
2142. feature "ATXN7L2" (0.000028)
2143. feature "RMI2" (0.000028)
2144. feature "FADS2" (0.000028)
2145. feature "ATP1A1-AS1" (0.000028)
2146. feature "SLC25A51P4" (0.000028)
2147. feature "ANKRD44" (0.000028)
2148. feature "FHL3" (0.000028)
2149. feature "FAM53C" (0.000028)
2150. feature "SCN7A" (0.000028)
2151. feature "SPHK2" (0.000028)
2152. feature "FBXO8" (0.000028)
2153. feature "PDLIM3" (0.000028)
2154. feature "ZNF182" (0.000027)
2155. feature "AMDHD2" (0.000027)
2156. feature "PSPN" (0.000027)
2157. feature "ST13P7" (0.000027)
2158. feature "TMEM241" (0.000027)
2159. feature "ADM2" (0.000027)
2160. feature "MED11" (0.000027)
2161. feature "HOXC8" (0.000027)
2162. feature "DRD4" (0.000027)
2163. feature "LINC01637" (0.000027)
2164. feature "EPOP" (0.000027)
2165. feature "ZNF469" (0.000027)
2166. feature "CCL26" (0.000027)
2167. feature "SRC" (0.000027)
2168. feature "PRPF40B" (0.000027)
2169. feature "GBP1" (0.000027)
2170. feature "TUBD1" (0.000027)
2171. feature "C19orf44" (0.000027)
2172. feature "FARS2" (0.000027)
2173. feature "TBC1D25" (0.000027)
2174. feature "MPRIP-AS1" (0.000027)
2175. feature "STON1" (0.000027)
2176. feature "HAGLROS" (0.000027)
2177. feature "HBM" (0.000027)
2178. feature "UAP1L1" (0.000027)
2179. feature "DNAH7" (0.000027)
2180. feature "NES" (0.000027)
2181. feature "NR2F2-AS1" (0.000027)
2182. feature "MTND2P28" (0.000027)
2183. feature "SIMC1" (0.000026)
2184. feature "RHOXF2B" (0.000026)
2185. feature "MYOM3" (0.000026)
2186. feature "CBWD6" (0.000026)
2187. feature "SLC25A12" (0.000026)
2188. feature "TDO2" (0.000026)
2189. feature "SLC1A2" (0.000026)
2190. feature "ARHGEF35" (0.000026)
2191. feature "CASC2" (0.000026)
2192. feature "IPPK" (0.000026)
2193. feature "CMTM3" (0.000026)
2194. feature "CYP2C9" (0.000026)
2195. feature "PDE1B" (0.000026)
2196. feature "LINC01963" (0.000026)
2197. feature "TOR2A" (0.000026)
2198. feature "KLRK1-AS1" (0.000026)
2199. feature "CLDN15" (0.000026)
2200. feature "MORN3" (0.000026)
2201. feature "LRRC3" (0.000026)
2202. feature "LINC02298" (0.000026)
2203. feature "CHST5" (0.000026)
2204. feature "MEGF6" (0.000025)
2205. feature "MSI1" (0.000025)
2206. feature "CHST14" (0.000025)
2207. feature "NUDT16L2P" (0.000025)
2208. feature "CUTC" (0.000025)
2209. feature "FAM241B" (0.000025)
2210. feature "EHD3" (0.000025)
2211. feature "HOXB3" (0.000025)
2212. feature "MT-TT" (0.000025)
2213. feature "LINC02178" (0.000025)
2214. feature "PDK2" (0.000025)
2215. feature "FSBP" (0.000025)
2216. feature "ZNF568" (0.000025)
2217. feature "AP1G2" (0.000025)
2218. feature "LINC01139" (0.000025)
2219. feature "HCP5" (0.000025)
2220. feature "PARP12" (0.000025)
2221. feature "KLHL24" (0.000025)
2222. feature "GTF2IRD1" (0.000025)
2223. feature "CUL9" (0.000025)
2224. feature "CACNB1" (0.000025)
2225. feature "ABHD12B" (0.000025)
2226. feature "UST" (0.000025)
2227. feature "WNT4" (0.000024)
2228. feature "LINC02018" (0.000024)
2229. feature "TBC1D22A-AS1" (0.000024)
2230. feature "OVOL2" (0.000024)
2231. feature "UQCRB-AS1" (0.000024)
2232. feature "ARMCX5" (0.000024)
2233. feature "PCAT14" (0.000024)
2234. feature "TMEM102" (0.000024)
2235. feature "KCNMB4" (0.000024)
2236. feature "C17orf99" (0.000024)
2237. feature "TRAF2" (0.000024)
2238. feature "SPRR3" (0.000024)
2239. feature "CDX2" (0.000024)
2240. feature "MTCO1P40" (0.000024)
2241. feature "SLC2A6" (0.000024)
2242. feature "HBA1" (0.000024)
2243. feature "PAX6" (0.000024)
2244. feature "ADO" (0.000024)
2245. feature "TMEM231" (0.000024)
2246. feature "ZNF417" (0.000024)
2247. feature "SERGEF" (0.000024)
2248. feature "ZNF416" (0.000024)
2249. feature "MT-TD" (0.000024)
2250. feature "AHCYL2" (0.000024)
2251. feature "C5AR1" (0.000024)
2252. feature "MARCHF3" (0.000024)
2253. feature "TGDS" (0.000024)
2254. feature "LINC01902" (0.000024)
2255. feature "BOK" (0.000024)
2256. feature "PRKAA2" (0.000024)
2257. feature "CACNB2" (0.000024)
2258. feature "TUBB3" (0.000024)
2259. feature "CAB39L" (0.000023)
2260. feature "ZNF433" (0.000023)
2261. feature "CAVIN2" (0.000023)
2262. feature "PHF1" (0.000023)
2263. feature "HERPUD2-AS1" (0.000023)
2264. feature "ESRRA" (0.000023)
2265. feature "MAU2" (0.000023)
2266. feature "NDN" (0.000023)
2267. feature "ZSCAN9" (0.000023)
2268. feature "FAM110C" (0.000023)
2269. feature "FAM166C" (0.000023)
2270. feature "FCRL5" (0.000023)
2271. feature "PPFIA3" (0.000023)
2272. feature "MT1F" (0.000023)
2273. feature "ARMC5" (0.000023)
2274. feature "ARL14" (0.000023)
2275. feature "NXT2" (0.000023)
2276. feature "DMD" (0.000023)
2277. feature "NFKBID" (0.000023)
2278. feature "LINC01546" (0.000023)
2279. feature "SYCE1L" (0.000023)
2280. feature "DUSP9" (0.000023)
2281. feature "CFAP410" (0.000022)
2282. feature "CPB1" (0.000022)
2283. feature "ZBED8" (0.000022)
2284. feature "RNF215" (0.000022)
2285. feature "CCDC97" (0.000022)
2286. feature "ABO" (0.000022)
2287. feature "USP30" (0.000022)
2288. feature "PPP2R3B" (0.000022)
2289. feature "PALM" (0.000022)
2290. feature "FAM131A" (0.000022)
2291. feature "TMTC4" (0.000022)
2292. feature "UBAC2" (0.000022)
2293. feature "ZNF747" (0.000022)
2294. feature "DOCK8-AS1" (0.000022)
2295. feature "ZNF418" (0.000022)
2296. feature "PLD1" (0.000022)
2297. feature "PLEKHG6" (0.000022)
2298. feature "MEX3A" (0.000022)
2299. feature "LRRC23" (0.000022)
2300. feature "ADGRE1" (0.000022)
2301. feature "SNX33" (0.000022)
2302. feature "TRANK1" (0.000022)
2303. feature "ROBO1" (0.000022)
2304. feature "LINC01679" (0.000022)
2305. feature "CUEDC1" (0.000022)
2306. feature "VASH2" (0.000022)
2307. feature "MOSPD3" (0.000022)
2308. feature "ADA2" (0.000022)
2309. feature "MB" (0.000022)
2310. feature "SNORA66" (0.000022)
2311. feature "MFAP3L" (0.000022)
2312. feature "DENND2D" (0.000022)
2313. feature "RDH14" (0.000021)
2314. feature "TRG-AS1" (0.000021)
2315. feature "POMK" (0.000021)
2316. feature "MORN1" (0.000021)
2317. feature "FGF22" (0.000021)
2318. feature "ZNF341" (0.000021)
2319. feature "NANOS1" (0.000021)
2320. feature "FOXF2" (0.000021)
2321. feature "RAPGEF6" (0.000021)
2322. feature "LASTR" (0.000021)
2323. feature "FTHL17" (0.000021)
2324. feature "ZNF263" (0.000021)
2325. feature "PRRX2" (0.000021)
2326. feature "YJEFN3" (0.000021)
2327. feature "HOXA-AS2" (0.000021)
2328. feature "PIGCP1" (0.000021)
2329. feature "CCDC15-DT" (0.000021)
2330. feature "LRTM1" (0.000021)
2331. feature "AQP3" (0.000021)
2332. feature "PBX4" (0.000021)
2333. feature "ITGBL1" (0.000021)
2334. feature "KAT2B" (0.000021)
2335. feature "MAP4K1" (0.000021)
2336. feature "IL15RA" (0.000021)
2337. feature "HDDC3" (0.000021)
2338. feature "SPDYE3" (0.000021)
2339. feature "ABTB2" (0.000021)
2340. feature "SPART-AS1" (0.000021)
2341. feature "MT1A" (0.000021)
2342. feature "SLC41A3" (0.000021)
2343. feature "LINC01666" (0.000021)
2344. feature "SEPTIN1" (0.000021)
2345. feature "FAM86C2P" (0.000021)
2346. feature "TBC1D10A" (0.000021)
2347. feature "GRK4" (0.000021)
2348. feature "PAQR9-AS1" (0.000021)
2349. feature "SPRY1" (0.000021)
2350. feature "SCRN2" (0.000020)
2351. feature "RAB30" (0.000020)
2352. feature "GPRIN2" (0.000020)
2353. feature "CLHC1" (0.000020)
2354. feature "C19orf81" (0.000020)
2355. feature "EPHX2" (0.000020)
2356. feature "DLGAP1-AS2" (0.000020)
2357. feature "KBTBD4" (0.000020)
2358. feature "AGAP1-IT1" (0.000020)
2359. feature "PCOLCE" (0.000020)
2360. feature "CORO7" (0.000020)
2361. feature "HSPD1P11" (0.000020)
2362. feature "TCAIM" (0.000020)
2363. feature "MT-TM" (0.000020)
2364. feature "AXIN2" (0.000020)
2365. feature "SH3BP5L" (0.000020)
2366. feature "TBC1D19" (0.000020)
2367. feature "PRRG2" (0.000020)
2368. feature "ZNF169" (0.000020)
2369. feature "NLRP10" (0.000020)
2370. feature "MSTO1" (0.000020)
2371. feature "ZNF879" (0.000020)
2372. feature "DPYSL3" (0.000020)
2373. feature "PBLD" (0.000020)
2374. feature "ZKSCAN4" (0.000020)
2375. feature "TMCO6" (0.000020)
2376. feature "RPS6KL1" (0.000020)
2377. feature "LRP4" (0.000020)
2378. feature "PITPNC1" (0.000020)
2379. feature "CBLL1-AS1" (0.000020)
2380. feature "LBHD2" (0.000020)
2381. feature "RAB11FIP4" (0.000020)
2382. feature "COL1A1" (0.000019)
2383. feature "S100P" (0.000019)
2384. feature "HLA-V" (0.000019)
2385. feature "PROS1" (0.000019)
2386. feature "TRAPPC6A" (0.000019)
2387. feature "CYB561D1" (0.000019)
2388. feature "IL21R" (0.000019)
2389. feature "UPF3AP2" (0.000019)
2390. feature "P2RX7" (0.000019)
2391. feature "RHOXF2" (0.000019)
2392. feature "RSAD1" (0.000019)
2393. feature "YPEL2" (0.000019)
2394. feature "DANT2" (0.000019)
2395. feature "TIGD2" (0.000019)
2396. feature "MICB" (0.000019)
2397. feature "RCCD1" (0.000019)
2398. feature "PGBD2" (0.000019)
2399. feature "MYO18B" (0.000019)
2400. feature "NPIPB4" (0.000019)
2401. feature "SYNM" (0.000019)
2402. feature "WDR35" (0.000019)
2403. feature "IFI35" (0.000019)
2404. feature "TINF2" (0.000018)
2405. feature "CLCF1" (0.000018)
2406. feature "LRCH4" (0.000018)
2407. feature "CRACDL" (0.000018)
2408. feature "USF1" (0.000018)
2409. feature "ACVR1C" (0.000018)
2410. feature "GLIPR1" (0.000018)
2411. feature "LPAR5" (0.000018)
2412. feature "ARHGEF17" (0.000018)
2413. feature "CNPY2-AS1" (0.000018)
2414. feature "ZNF90P3" (0.000018)
2415. feature "YBEY" (0.000018)
2416. feature "RPL5P3" (0.000018)
2417. feature "NKX2-8" (0.000018)
2418. feature "MAP3K10" (0.000018)
2419. feature "HS1BP3" (0.000018)
2420. feature "LINC02273" (0.000018)
2421. feature "LSP1P4" (0.000018)
2422. feature "C2orf27A" (0.000018)
2423. feature "SNX11" (0.000018)
2424. feature "ADAM19" (0.000018)
2425. feature "IRAK3" (0.000018)
2426. feature "OR2A5" (0.000018)
2427. feature "NPEPL1" (0.000018)
2428. feature "HDAC5" (0.000018)
2429. feature "LINC01843" (0.000018)
2430. feature "DISP1" (0.000018)
2431. feature "DAB2IP" (0.000018)
2432. feature "CCDC40" (0.000018)
2433. feature "ENKD1" (0.000018)
2434. feature "SLC31A2" (0.000018)
2435. feature "KATNB1" (0.000018)
2436. feature "MIR137HG" (0.000018)
2437. feature "BCKDHB" (0.000018)
2438. feature "RAB15" (0.000018)
2439. feature "LINC01087" (0.000017)
2440. feature "DYNLT2" (0.000017)
2441. feature "ZNF256" (0.000017)
2442. feature "SORBS1" (0.000017)
2443. feature "NEK8" (0.000017)
2444. feature "LRRC20" (0.000017)
2445. feature "GREP1" (0.000017)
2446. feature "MUC4" (0.000017)
2447. feature "GOLGA6L2" (0.000017)
2448. feature "MIR663B" (0.000017)
2449. feature "FAM81A" (0.000017)
2450. feature "HSP90AA4P" (0.000017)
2451. feature "MIOS-DT" (0.000017)
2452. feature "ALX1" (0.000017)
2453. feature "C16orf95" (0.000017)
2454. feature "LHX6" (0.000017)
2455. feature "OSER1-DT" (0.000017)
2456. feature "MBTPS1-DT" (0.000017)
2457. feature "DPPA4" (0.000017)
2458. feature "TICAM1" (0.000017)
2459. feature "LINC02672" (0.000017)
2460. feature "ROGDI" (0.000017)
2461. feature "PECAM1" (0.000017)
2462. feature "SLC35A2" (0.000017)
2463. feature "ALDH6A1" (0.000017)
2464. feature "CLMN" (0.000017)
2465. feature "SLC2A3" (0.000017)
2466. feature "CXCR2" (0.000017)
2467. feature "FZD8" (0.000017)
2468. feature "TBC1D22A-DT" (0.000017)
2469. feature "MAP7D2" (0.000017)
2470. feature "NOMO3" (0.000017)
2471. feature "ADPRM" (0.000017)
2472. feature "SNN" (0.000017)
2473. feature "STK36" (0.000017)
2474. feature "C1QL1" (0.000017)
2475. feature "CCDC191" (0.000017)
2476. feature "RPL24P9" (0.000017)
2477. feature "HEIH" (0.000016)
2478. feature "ZNF782" (0.000016)
2479. feature "TMEM125" (0.000016)
2480. feature "FGD1" (0.000016)
2481. feature "ZNF212" (0.000016)
2482. feature "MEF2D" (0.000016)
2483. feature "RADIL" (0.000016)
2484. feature "FBXO43" (0.000016)
2485. feature "TMEM144" (0.000016)
2486. feature "ABHD14B" (0.000016)
2487. feature "EEPD1" (0.000016)
2488. feature "CSPG4P12" (0.000016)
2489. feature "RINL" (0.000016)
2490. feature "ZNF826P" (0.000016)
2491. feature "ANO7L1" (0.000016)
2492. feature "PKHD1L1" (0.000016)
2493. feature "SPATA6L" (0.000016)
2494. feature "NLRC5" (0.000016)
2495. feature "LRRC75B" (0.000016)
2496. feature "IL1A" (0.000016)
2497. feature "LMX1B" (0.000016)
2498. feature "COX4I2" (0.000016)
2499. feature "HMGN1P17" (0.000016)
2500. feature "LY6G5C" (0.000016)
2501. feature "PRR36" (0.000016)
2502. feature "SMCO4" (0.000016)
2503. feature "FBXL16" (0.000016)
2504. feature "HPCA" (0.000016)
2505. feature "LYPD6" (0.000016)
2506. feature "TLE2" (0.000016)
2507. feature "PFKFB4" (0.000015)
2508. feature "EVC2" (0.000015)
2509. feature "MAILR" (0.000015)
2510. feature "C21orf62-AS1" (0.000015)
2511. feature "GASAL1" (0.000015)
2512. feature "CNEP1R1" (0.000015)
2513. feature "BMP4" (0.000015)
2514. feature "FSIP1" (0.000015)
2515. feature "ARTN" (0.000015)
2516. feature "OSCP1" (0.000015)
2517. feature "PFKFB2" (0.000015)
2518. feature "IKBKE" (0.000015)
2519. feature "RNF166" (0.000015)
2520. feature "ITGA11" (0.000015)
2521. feature "OR10K1" (0.000015)
2522. feature "LINC00320" (0.000015)
2523. feature "GBP2" (0.000015)
2524. feature "LINC02894" (0.000015)
2525. feature "C11orf98" (0.000015)
2526. feature "EEF1A1P25" (0.000015)
2527. feature "GRAMD1B" (0.000015)
2528. feature "CTIF" (0.000015)
2529. feature "MS4A10" (0.000015)
2530. feature "KCNMA1" (0.000015)
2531. feature "SERPINF1" (0.000015)
2532. feature "EFCAB1" (0.000015)
2533. feature "TYMSOS" (0.000015)
2534. feature "NFKBIE" (0.000015)
2535. feature "NKX2-1" (0.000015)
2536. feature "KIF21B" (0.000015)
2537. feature "FLNC" (0.000015)
2538. feature "LINC02664" (0.000015)
2539. feature "SH3GL3" (0.000015)
2540. feature "LINC01102" (0.000015)
2541. feature "OXSM" (0.000015)
2542. feature "NR1D1" (0.000014)
2543. feature "DUOX1" (0.000014)
2544. feature "CLDND2" (0.000014)
2545. feature "BDH2" (0.000014)
2546. feature "FNBP1P1" (0.000014)
2547. feature "BMI1" (0.000014)
2548. feature "PSMD10P2" (0.000014)
2549. feature "ADGRF4" (0.000014)
2550. feature "SLC38A4" (0.000014)
2551. feature "RPL9P32" (0.000014)
2552. feature "NOP14-AS1" (0.000014)
2553. feature "AMZ1" (0.000014)
2554. feature "LINC01266" (0.000014)
2555. feature "TRAM1L1" (0.000014)
2556. feature "PHC1" (0.000014)
2557. feature "LINC01607" (0.000014)
2558. feature "MYO1F" (0.000014)
2559. feature "ZSCAN18" (0.000014)
2560. feature "LINC01460" (0.000014)
2561. feature "LTBP1" (0.000014)
2562. feature "HAGLR" (0.000014)
2563. feature "PLA2G6" (0.000014)
2564. feature "HDAC11" (0.000014)
2565. feature "H2AW" (0.000014)
2566. feature "LTBP2" (0.000014)
2567. feature "SIGLEC10" (0.000014)
2568. feature "ACTR1B" (0.000014)
2569. feature "MAP3K2-DT" (0.000014)
2570. feature "PROC" (0.000014)
2571. feature "CAPNS2" (0.000014)
2572. feature "NECTIN4" (0.000014)
2573. feature "LTB4R" (0.000014)
2574. feature "PSKH1" (0.000014)
2575. feature "HECW2" (0.000014)
2576. feature "INTS6-AS1" (0.000014)
2577. feature "ATL1" (0.000014)
2578. feature "ANXA6" (0.000014)
2579. feature "FOXD1" (0.000014)
2580. feature "NLGN2" (0.000014)
2581. feature "TUBA1A" (0.000013)
2582. feature "B4GALNT2" (0.000013)
2583. feature "ACVR1" (0.000013)
2584. feature "FAM111A-DT" (0.000013)
2585. feature "YWHAEP5" (0.000013)
2586. feature "BATF3" (0.000013)
2587. feature "RGL3" (0.000013)
2588. feature "ASCL2" (0.000013)
2589. feature "TPRG1" (0.000013)
2590. feature "RNF32" (0.000013)
2591. feature "IFITM10" (0.000013)
2592. feature "MAN1A1" (0.000013)
2593. feature "CDH24" (0.000013)
2594. feature "TRIM45" (0.000013)
2595. feature "DNAJC3-DT" (0.000013)
2596. feature "RN7SL471P" (0.000013)
2597. feature "MATN2" (0.000013)
2598. feature "RAB9A" (0.000013)
2599. feature "TRDN-AS1" (0.000013)
2600. feature "ZBTB47" (0.000013)
2601. feature "LBX2" (0.000013)
2602. feature "NSA2P4" (0.000013)
2603. feature "TGM2" (0.000013)
2604. feature "CFAP45" (0.000013)
2605. feature "MESP1" (0.000013)
2606. feature "GAB1" (0.000013)
2607. feature "RRAGD" (0.000013)
2608. feature "FZD1" (0.000012)
2609. feature "NLRP8" (0.000012)
2610. feature "FAM43A" (0.000012)
2611. feature "TM7SF2" (0.000012)
2612. feature "SPX" (0.000012)
2613. feature "RCBTB2" (0.000012)
2614. feature "COL19A1" (0.000012)
2615. feature "ABCA7" (0.000012)
2616. feature "IQCH-AS1" (0.000012)
2617. feature "LIPE-AS1" (0.000012)
2618. feature "IL11" (0.000012)
2619. feature "WFDC3" (0.000012)
2620. feature "ARID5A" (0.000012)
2621. feature "CTBS" (0.000012)
2622. feature "RCCD1-AS1" (0.000012)
2623. feature "ANKMY1" (0.000012)
2624. feature "ADCK1" (0.000012)
2625. feature "STARD13-AS" (0.000012)
2626. feature "NUAK2" (0.000012)
2627. feature "TMEM79" (0.000012)
2628. feature "CCDC141" (0.000012)
2629. feature "HHLA2" (0.000012)
2630. feature "CNOT6LP1" (0.000012)
2631. feature "PLAAT4" (0.000012)
2632. feature "LINC01816" (0.000012)
2633. feature "CSAG3" (0.000012)
2634. feature "RPL12P42" (0.000012)
2635. feature "NTF4" (0.000012)
2636. feature "MSRB3" (0.000012)
2637. feature "CCPG1" (0.000012)
2638. feature "RPL23AP7" (0.000012)
2639. feature "H1-6" (0.000012)
2640. feature "ACBD4" (0.000012)
2641. feature "DALRD3" (0.000012)
2642. feature "LAX1" (0.000012)
2643. feature "MT-TF" (0.000012)
2644. feature "AKAP6" (0.000012)
2645. feature "TTC30A" (0.000012)
2646. feature "CES1" (0.000012)
2647. feature "LONRF1" (0.000012)
2648. feature "HOXA7" (0.000012)
2649. feature "TNC" (0.000012)
2650. feature "ROR2" (0.000012)
2651. feature "LRRC2" (0.000011)
2652. feature "GPRC5D" (0.000011)
2653. feature "MIR194-2HG" (0.000011)
2654. feature "ZNF99" (0.000011)
2655. feature "ACSF2" (0.000011)
2656. feature "LINC00911" (0.000011)
2657. feature "TTLL1" (0.000011)
2658. feature "YBX1P6" (0.000011)
2659. feature "TMEM128" (0.000011)
2660. feature "PSD3" (0.000011)
2661. feature "BMP2" (0.000011)
2662. feature "DGCR6" (0.000011)
2663. feature "ZNF76" (0.000011)
2664. feature "AQP11" (0.000011)
2665. feature "FOXO4" (0.000011)
2666. feature "ST3GAL6" (0.000011)
2667. feature "REP15" (0.000011)
2668. feature "PANK1" (0.000011)
2669. feature "LINC01615" (0.000011)
2670. feature "FUT2" (0.000011)
2671. feature "XKR8" (0.000011)
2672. feature "LINC02541" (0.000011)
2673. feature "GHDC" (0.000011)
2674. feature "LINC02029" (0.000011)
2675. feature "AMBRA1" (0.000011)
2676. feature "ELOVL7" (0.000011)
2677. feature "NAPA-AS1" (0.000011)
2678. feature "LINC02615" (0.000011)
2679. feature "LCAT" (0.000011)
2680. feature "CARF" (0.000011)
2681. feature "ARL15" (0.000011)
2682. feature "CABP4" (0.000011)
2683. feature "CMYA5" (0.000011)
2684. feature "PWWP2B" (0.000010)
2685. feature "DMRTA2" (0.000010)
2686. feature "MT-TI" (0.000010)
2687. feature "METAP1D" (0.000010)
2688. feature "CTHRC1" (0.000010)
2689. feature "HOXC6" (0.000010)
2690. feature "CTF1" (0.000010)
2691. feature "IL10RB" (0.000010)
2692. feature "NOVA2" (0.000010)
2693. feature "TLR2" (0.000010)
2694. feature "MPG" (0.000010)
2695. feature "FTH1P16" (0.000010)
2696. feature "MAN1C1" (0.000010)
2697. feature "NDST2" (0.000010)
2698. feature "OR10D3" (0.000010)
2699. feature "CYP2J2" (0.000010)
2700. feature "HSD11B2" (0.000010)
2701. feature "ZNF571-AS1" (0.000010)
2702. feature "ZNRF2" (0.000010)
2703. feature "LINC02245" (0.000010)
2704. feature "FDXACB1" (0.000010)
2705. feature "HSPD1P5" (0.000010)
2706. feature "CCDC9B" (0.000010)
2707. feature "ARHGEF10L" (0.000010)
2708. feature "LINC01667" (0.000010)
2709. feature "WNT7A" (0.000010)
2710. feature "MUS81" (0.000010)
2711. feature "NTRK2" (0.000010)
2712. feature "SLC2A10" (0.000010)
2713. feature "UNC13C" (0.000010)
2714. feature "LINC01764" (0.000010)
2715. feature "ST3GAL1-DT" (0.000010)
2716. feature "USP20" (0.000010)
2717. feature "POTEC" (0.000010)
2718. feature "SRP14-DT" (0.000009)
2719. feature "PARD6A" (0.000009)
2720. feature "DOC2A" (0.000009)
2721. feature "C3orf35" (0.000009)
2722. feature "OCEL1" (0.000009)
2723. feature "KMT2E-AS1" (0.000009)
2724. feature "CCR12P" (0.000009)
2725. feature "SMCR5" (0.000009)
2726. feature "ABCC13" (0.000009)
2727. feature "SLAMF9" (0.000009)
2728. feature "FGFBP3" (0.000009)
2729. feature "PARP16" (0.000009)
2730. feature "ROBO3" (0.000009)
2731. feature "SLC6A9" (0.000009)
2732. feature "MAP3K8" (0.000009)
2733. feature "ZFYVE1" (0.000009)
2734. feature "SLC30A6-DT" (0.000009)
2735. feature "TRAIP" (0.000009)
2736. feature "ARMCX6" (0.000009)
2737. feature "ARMCX4" (0.000009)
2738. feature "N4BP2L2-IT2" (0.000009)
2739. feature "TMEM182" (0.000009)
2740. feature "ST20" (0.000009)
2741. feature "SAMD10" (0.000009)
2742. feature "GAREM2" (0.000009)
2743. feature "SERPINB10" (0.000009)
2744. feature "SDK1" (0.000009)
2745. feature "PGLS-DT" (0.000009)
2746. feature "RIMKLA" (0.000009)
2747. feature "KIAA2012-AS1" (0.000009)
2748. feature "LINC01415" (0.000009)
2749. feature "ATP8B2" (0.000009)
2750. feature "LRFN1" (0.000009)
2751. feature "NRXN2" (0.000009)
2752. feature "FOXE1" (0.000009)
2753. feature "MT1G" (0.000009)
2754. feature "GAS7" (0.000009)
2755. feature "LINC01533" (0.000009)
2756. feature "WDR24" (0.000008)
2757. feature "SNURF" (0.000008)
2758. feature "FER1L4" (0.000008)
2759. feature "CCDC153" (0.000008)
2760. feature "LINC01091" (0.000008)
2761. feature "LINC00683" (0.000008)
2762. feature "CATSPERB" (0.000008)
2763. feature "CAMK2B" (0.000008)
2764. feature "UFSP1" (0.000008)
2765. feature "PCYOX1L" (0.000008)
2766. feature "CD2AP-DT" (0.000008)
2767. feature "FAM166A" (0.000008)
2768. feature "NGEF" (0.000008)
2769. feature "GMIP" (0.000008)
2770. feature "SDSL" (0.000008)
2771. feature "SMO" (0.000008)
2772. feature "GLB1L" (0.000008)
2773. feature "ZNF132-DT" (0.000008)
2774. feature "ITGA1" (0.000008)
2775. feature "POTEF" (0.000008)
2776. feature "ABTB1" (0.000008)
2777. feature "NAALAD2" (0.000008)
2778. feature "SPATA2L" (0.000008)
2779. feature "GNGT1" (0.000008)
2780. feature "PTPRB" (0.000008)
2781. feature "CASC9" (0.000008)
2782. feature "SMOX" (0.000008)
2783. feature "TCL6" (0.000008)
2784. feature "RELB" (0.000008)
2785. feature "TNFRSF11A" (0.000008)
2786. feature "MTMR11" (0.000007)
2787. feature "ZNF577" (0.000007)
2788. feature "TUSC1" (0.000007)
2789. feature "RHOQP3" (0.000007)
2790. feature "ACADS" (0.000007)
2791. feature "TAF6L" (0.000007)
2792. feature "CRTAM" (0.000007)
2793. feature "CDK6-AS1" (0.000007)
2794. feature "C12orf50" (0.000007)
2795. feature "SLC2A8" (0.000007)
2796. feature "APOL3" (0.000007)
2797. feature "SPATA2" (0.000007)
2798. feature "LIMD2" (0.000007)
2799. feature "LARGE1" (0.000007)
2800. feature "RELN" (0.000007)
2801. feature "NUP62CL" (0.000007)
2802. feature "KANK3" (0.000007)
2803. feature "BMP8A" (0.000007)
2804. feature "ZBTB22" (0.000007)
2805. feature "TRPT1" (0.000007)
2806. feature "RASGRP2" (0.000007)
2807. feature "MIR320A" (0.000007)
2808. feature "PARS2" (0.000007)
2809. feature "ZNF517" (0.000007)
2810. feature "SLC9B1" (0.000007)
2811. feature "MT-TH" (0.000007)
2812. feature "MAD1L1" (0.000007)
2813. feature "STAG3L5P-PVRIG2P-PILRB" (0.000007)
2814. feature "STARD7-AS1" (0.000007)
2815. feature "KRBOX4" (0.000007)
2816. feature "MSH5" (0.000007)
2817. feature "TNFRSF11B" (0.000006)
2818. feature "SERTAD4-AS1" (0.000006)
2819. feature "KIAA2012" (0.000006)
2820. feature "NOTCH2NLC" (0.000006)
2821. feature "ART3" (0.000006)
2822. feature "CA11" (0.000006)
2823. feature "CHMP1B2P" (0.000006)
2824. feature "DET1" (0.000006)
2825. feature "ACP5" (0.000006)
2826. feature "TMEM158" (0.000006)
2827. feature "SREK1IP1P1" (0.000006)
2828. feature "ADTRP" (0.000006)
2829. feature "NARF-AS2" (0.000006)
2830. feature "LINC01772" (0.000006)
2831. feature "NOC2LP1" (0.000006)
2832. feature "MCMDC2" (0.000006)
2833. feature "LINC01560" (0.000006)
2834. feature "HPS6" (0.000006)
2835. feature "LINC01551" (0.000006)
2836. feature "BLOC1S6P1" (0.000006)
2837. feature "ATP1B2" (0.000006)
2838. feature "PHC2" (0.000006)
2839. feature "ZBTB46" (0.000006)
2840. feature "THBS1-IT1" (0.000006)
2841. feature "RPS12P16" (0.000006)
2842. feature "YWHAQP5" (0.000006)
2843. feature "LERFS" (0.000006)
2844. feature "MT-TK" (0.000006)
2845. feature "TK2" (0.000006)
2846. feature "CARD9" (0.000006)
2847. feature "DHX35-DT" (0.000006)
2848. feature "LCTL" (0.000006)
2849. feature "ANOS1" (0.000005)
2850. feature "FAT4" (0.000005)
2851. feature "PADI2" (0.000005)
2852. feature "LINC02899" (0.000005)
2853. feature "UPRT" (0.000005)
2854. feature "POLM" (0.000005)
2855. feature "PDE9A" (0.000005)
2856. feature "LINC01405" (0.000005)
2857. feature "GPR160" (0.000005)
2858. feature "BBS4" (0.000005)
2859. feature "ZNF25" (0.000005)
2860. feature "GVINP1" (0.000005)
2861. feature "SYT15" (0.000005)
2862. feature "SMC1B" (0.000005)
2863. feature "SLC9A3" (0.000005)
2864. feature "STIM2-AS1" (0.000005)
2865. feature "NUPR2" (0.000005)
2866. feature "LZTR1" (0.000005)
2867. feature "FABP3" (0.000005)
2868. feature "HHAT" (0.000005)
2869. feature "APLF" (0.000005)
2870. feature "FRAT2" (0.000004)
2871. feature "FAM131C" (0.000004)
2872. feature "DTNB" (0.000004)
2873. feature "C11orf71" (0.000004)
2874. feature "PRORSD1P" (0.000004)
2875. feature "RIMS2" (0.000004)
2876. feature "RSPH1" (0.000004)
2877. feature "ZP3" (0.000004)
2878. feature "MPPE1" (0.000004)
2879. feature "SAMMSON" (0.000004)
2880. feature "CRY2" (0.000004)
2881. feature "LRRC26" (0.000004)
2882. feature "UNC5B-AS1" (0.000004)
2883. feature "DUT-AS1" (0.000004)
2884. feature "ZBTB18" (0.000004)
2885. feature "TRNP1" (0.000004)
2886. feature "PADI1" (0.000004)
2887. feature "LINC00471" (0.000004)
2888. feature "RFTN1" (0.000004)
2889. feature "RPS18P9" (0.000004)
2890. feature "CREB3L1" (0.000004)
2891. feature "ANO8" (0.000004)
2892. feature "ASB2" (0.000004)
2893. feature "SLITRK2" (0.000004)
2894. feature "C14orf132" (0.000004)
2895. feature "CNDP1" (0.000004)
2896. feature "TNFRSF14" (0.000004)
2897. feature "NAP1L1P1" (0.000004)
2898. feature "KSR1" (0.000004)
2899. feature "MYH7B" (0.000004)
2900. feature "ITGA4" (0.000004)
2901. feature "TSNARE1" (0.000004)
2902. feature "MMP28" (0.000003)
2903. feature "LINC01840" (0.000003)
2904. feature "ANG" (0.000003)
2905. feature "FIBCD1" (0.000003)
2906. feature "CDHR3" (0.000003)
2907. feature "UQCRHL" (0.000003)
2908. feature "RPL34P1" (0.000003)
2909. feature "SLC8A1" (0.000003)
2910. feature "DENND4B" (0.000003)
2911. feature "GOLGA2P5" (0.000003)
2912. feature "FA2H" (0.000003)
2913. feature "NEURL1" (0.000003)
2914. feature "CCDC61" (0.000003)
2915. feature "CCDC62" (0.000003)
2916. feature "LGR6" (0.000003)
2917. feature "LOX" (0.000003)
2918. feature "MIF4GD-DT" (0.000003)
2919. feature "LENG9" (0.000003)
2920. feature "RNF123" (0.000003)
2921. feature "SMPDL3A" (0.000003)
2922. feature "MAP1LC3B2" (0.000003)
2923. feature "SHF" (0.000003)
2924. feature "LINC01854" (0.000003)
2925. feature "C12orf76" (0.000003)
2926. feature "PPIEL" (0.000003)
2927. feature "C17orf100" (0.000002)
2928. feature "PCDHB11" (0.000002)
2929. feature "ARHGEF33" (0.000002)
2930. feature "POTEM" (0.000002)
2931. feature "MYH15" (0.000002)
2932. feature "CRB3" (0.000002)
2933. feature "WWOX-AS1" (0.000002)
2934. feature "SUSD5" (0.000002)
2935. feature "GIHCG" (0.000002)
2936. feature "FBXO33" (0.000002)
2937. feature "STC1" (0.000002)
2938. feature "DYNC1LI2-DT" (0.000002)
2939. feature "PIM2" (0.000002)
2940. feature "ZNF682" (0.000002)
2941. feature "RNF227" (0.000002)
2942. feature "OR52A1" (0.000002)
2943. feature "NEK10" (0.000002)
2944. feature "TAF1C" (0.000002)
2945. feature "REEP1" (0.000002)
2946. feature "INTS9" (0.000002)
2947. feature "PPIAP46" (0.000001)
2948. feature "CTU2" (0.000001)
2949. feature "PPFIA4" (0.000001)
2950. feature "PPP2R5B" (0.000001)
2951. feature "ZNF687-AS1" (0.000001)
2952. feature "PAN2" (0.000001)
2953. feature "RPS15AP29" (0.000001)
2954. feature "HOXA4" (0.000001)
2955. feature "BMP8B" (0.000001)
2956. feature "CDKN1C" (0.000000)
2957. feature "SLC30A10" (0.000000)
2958. feature "LINC01695" (0.000000)
2959. feature "SUNO1" (0.000000)
2960. feature "QPCTL" (0.000000)
2961. feature "OR4K17" (0.000000)
2962. feature "BBS12" (0.000000)
2963. feature "NLRP2B" (0.000000)
2964. feature "FOXO3B" (0.000000)
2965. feature "SULT2A1" (0.000000)
2966. feature "NAT1" (0.000000)
2967. feature "ACTBP2" (0.000000)
2968. feature "COL9A2" (0.000000)
2969. feature "NOD1" (0.000000)
2970. feature "C4orf46" (0.000000)
2971. feature "SYNPO2L" (0.000000)
2972. feature "LRRC61" (0.000000)
2973. feature "LINC00663" (0.000000)
2974. feature "EDAR" (0.000000)
2975. feature "ZCRB1P1" (0.000000)
2976. feature "RAB42" (0.000000)
2977. feature "ITPRIP" (0.000000)
2978. feature "ZNF837" (0.000000)
2979. feature "ODAD2" (0.000000)
2980. feature "TRPM2-AS" (0.000000)
2981. feature "HOXB2" (0.000000)
2982. feature "TP53INP2" (0.000000)
2983. feature "H3C12" (0.000000)
2984. feature "CABLES1" (0.000000)
2985. feature "FLACC1" (0.000000)
2986. feature "NRSN1" (0.000000)
2987. feature "TLX3" (0.000000)
2988. feature "INCA1" (0.000000)
2989. feature "NUP153-AS1" (0.000000)
2990. feature "ZDHHC1" (0.000000)
2991. feature "TMPOP2" (0.000000)
2992. feature "ZFYVE28" (0.000000)
2993. feature "THSD7A" (0.000000)
2994. feature "ECHDC3" (0.000000)
2995. feature "TMCC2" (0.000000)
2996. feature "NUDT18" (0.000000)
2997. feature "LINC01687" (0.000000)
2998. feature "RAB17" (0.000000)
2999. feature "WWTR1-AS1" (0.000000)
3000. feature "FAM214B" (0.000000)

¶

4.4.3) XGBoost

Also in this case we tried to see if the Xgboost predictor could beat the performance of the Random forest classifier.

Indeed it happened, we built the model as the previous one, the parameters in the grid search are the same as the Random forest, only the max depth was decreased.

It achieves a performance of 94,91%, that up to us is an acceptable score as this dataset seems the most difficult to make predictions.

In [ ]:
#i want to build an XGBoost classifier for the Dropseq HCC1806 dataset where random forest classifier has the worst performance

# Define the XGBoost classifier
xgb = XGBClassifier(random_state=42)

# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [100, 150], 'max_depth': [30, 50, 60]}

grid_search = GridSearchCV(xgb, param_grid, cv=5)

outer_cv = KFold(n_splits=5, shuffle=True, random_state=42) 

# Perform the cross validation
scores = cross_val_score(grid_search, X_train_df_DS_HCC1806, y_train_df_DS_HCC1806, cv=outer_cv)

# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))

# Perform GridSearchCV on the train dataset to get the best parameters and fit the model
grid_search.fit(X_train_df_DS_HCC1806, y_train_df_DS_HCC1806)

print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
print("Best model: ", grid_search.best_estimator_)

print("Test accuracy: ", grid_search.best_estimator_.score(X_test_df_DS_HCC1806, y_test_df_DS_HCC1806))

# Print the feature importances
importances = grid_search.best_estimator_.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_df_DS_HCC1806.shape[1]):
    print("%d. feature %s (%f)" % (f + 1, X_train_df_DS_HCC1806.columns[indices[f]], importances[indices[f]]))

best_xgb_DS_HCC1806 = grid_search.best_estimator_

Mean accuracy of nested cross validation:  0.9436603803951641
Best hyperparameters:  {'max_depth': 30, 'n_estimators': 150}
Best accuracy:  0.9441475191002301
Best model:  XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=30, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=150, n_jobs=None,
              num_parallel_tree=None, random_state=42, ...)
Test accuracy:  0.9491486946651533
Feature ranking:
1. feature "NDRG1" (0.070036)
2. feature "AKR1C2" (0.017882)
3. feature "FGF23" (0.015801)
4. feature "IGFBP3" (0.014589)
5. feature "EGLN3" (0.014030)
6. feature "AKR1C1" (0.010664)
7. feature "PLEKHF1" (0.008650)
8. feature "ECH1" (0.008633)
9. feature "WDR43" (0.008219)
10. feature "RNF20" (0.008062)
11. feature "BNIP3" (0.007771)
12. feature "RGPD4-AS1" (0.007295)
13. feature "SART1" (0.007176)
14. feature "GPM6A" (0.006632)
15. feature "RNF122" (0.006158)
16. feature "LDHA" (0.006072)
17. feature "PUSL1" (0.005928)
18. feature "P4HA1" (0.005924)
19. feature "VIT" (0.005616)
20. feature "INO80E" (0.005482)
21. feature "CCNG2" (0.005472)
22. feature "MT-TV" (0.005431)
23. feature "ANGPTL4" (0.005312)
24. feature "EIF3J" (0.005251)
25. feature "AXL" (0.004537)
26. feature "AMOTL1" (0.004435)
27. feature "POLR2J" (0.004342)
28. feature "LUZP1" (0.004263)
29. feature "RPS26" (0.004192)
30. feature "FPGS" (0.004055)
31. feature "FOSL1" (0.003959)
32. feature "MT-ATP8" (0.003931)
33. feature "BTBD9" (0.003924)
34. feature "HS6ST1" (0.003752)
35. feature "RHOD" (0.003671)
36. feature "KMT2D" (0.003664)
37. feature "BCYRN1" (0.003604)
38. feature "HSPD1" (0.003604)
39. feature "FNDC10" (0.003538)
40. feature "TMSB10" (0.003503)
41. feature "BNIP3L" (0.003485)
42. feature "YKT6" (0.003472)
43. feature "ITSN2" (0.003423)
44. feature "COX8A" (0.003421)
45. feature "ARHGDIA" (0.003357)
46. feature "TCF7L1" (0.003353)
47. feature "COX14" (0.003336)
48. feature "NPM1P40" (0.003281)
49. feature "MT-CYB" (0.003264)
50. feature "RFK" (0.003206)
51. feature "PGBD1" (0.003154)
52. feature "NEUROD1" (0.003069)
53. feature "STC2" (0.003052)
54. feature "DDIT4" (0.003027)
55. feature "NEAT1" (0.002973)
56. feature "PGK1" (0.002968)
57. feature "DIO2" (0.002935)
58. feature "CAVIN3" (0.002899)
59. feature "OIP5-AS1" (0.002835)
60. feature "TPD52L1" (0.002831)
61. feature "TOP2A" (0.002822)
62. feature "TAF15" (0.002808)
63. feature "CEP63" (0.002793)
64. feature "DRAP1" (0.002748)
65. feature "S100A10" (0.002747)
66. feature "B4GALT1" (0.002716)
67. feature "MLLT3" (0.002694)
68. feature "MRFAP1" (0.002648)
69. feature "AK4" (0.002641)
70. feature "RPL41" (0.002634)
71. feature "ATN1" (0.002625)
72. feature "PIN4" (0.002613)
73. feature "SCP2" (0.002582)
74. feature "DNM2" (0.002567)
75. feature "NCL" (0.002551)
76. feature "ACSL4" (0.002541)
77. feature "RIPK1" (0.002532)
78. feature "APOOL" (0.002525)
79. feature "MT-ND6" (0.002510)
80. feature "CDT1" (0.002510)
81. feature "FEM1A" (0.002459)
82. feature "FAM114A1" (0.002455)
83. feature "LIMCH1" (0.002446)
84. feature "KIAA1522" (0.002432)
85. feature "HSP90AA1" (0.002413)
86. feature "CACNA1A" (0.002396)
87. feature "PTOV1" (0.002369)
88. feature "PRPF31" (0.002358)
89. feature "KLF6" (0.002334)
90. feature "TOP2B" (0.002313)
91. feature "SLC2A4RG" (0.002304)
92. feature "PPP4R2" (0.002303)
93. feature "ZBTB20" (0.002298)
94. feature "UPF3B" (0.002294)
95. feature "RPL39" (0.002280)
96. feature "ING2" (0.002280)
97. feature "ZNF324" (0.002267)
98. feature "PPP2R5D" (0.002259)
99. feature "SECISBP2" (0.002244)
100. feature "UCA1" (0.002216)
101. feature "MORN2" (0.002203)
102. feature "ZCRB1" (0.002201)
103. feature "DCAF7" (0.002190)
104. feature "UBE2E1" (0.002187)
105. feature "HMGA1" (0.002181)
106. feature "MT1X" (0.002171)
107. feature "PROSER1" (0.002169)
108. feature "CEP55" (0.002159)
109. feature "JUP" (0.002149)
110. feature "DDX23" (0.002148)
111. feature "BOD1L1" (0.002137)
112. feature "ARSA" (0.002136)
113. feature "COTL1" (0.002135)
114. feature "TLE3" (0.002134)
115. feature "MRPS6" (0.002134)
116. feature "MYO5C" (0.002133)
117. feature "PHF3" (0.002117)
118. feature "ZC3H15" (0.002114)
119. feature "RBM23" (0.002111)
120. feature "RPS3" (0.002111)
121. feature "PA2G4P6" (0.002110)
122. feature "NCLN" (0.002106)
123. feature "VEGFB" (0.002105)
124. feature "LINC01133" (0.002091)
125. feature "METTL5" (0.002090)
126. feature "CNNM2" (0.002088)
127. feature "SEM1" (0.002080)
128. feature "CUL4B" (0.002080)
129. feature "DDIT3" (0.002075)
130. feature "CHTOP" (0.002060)
131. feature "SMARCC1" (0.002058)
132. feature "TINAGL1" (0.002053)
133. feature "GNAS" (0.002044)
134. feature "CETN3" (0.002036)
135. feature "CAPN1" (0.002036)
136. feature "GDPGP1" (0.002016)
137. feature "SETD2" (0.002010)
138. feature "CLIP1" (0.002010)
139. feature "ARID5B" (0.001991)
140. feature "H2BC9" (0.001987)
141. feature "CTNND1" (0.001981)
142. feature "STT3A" (0.001975)
143. feature "PRRC2A" (0.001960)
144. feature "RPL7A" (0.001939)
145. feature "PDCD4" (0.001938)
146. feature "SAMD15" (0.001933)
147. feature "C12orf75" (0.001931)
148. feature "PLBD2" (0.001925)
149. feature "H2BC4" (0.001921)
150. feature "HEPACAM" (0.001915)
151. feature "ZNF302" (0.001913)
152. feature "PLEC" (0.001911)
153. feature "NSMF" (0.001903)
154. feature "H19" (0.001892)
155. feature "SYNJ2" (0.001878)
156. feature "RPS27" (0.001874)
157. feature "CLIC1" (0.001871)
158. feature "VMA21" (0.001865)
159. feature "SERINC2" (0.001861)
160. feature "DAAM1" (0.001857)
161. feature "CEP120" (0.001851)
162. feature "APPL1" (0.001849)
163. feature "MLXIP" (0.001848)
164. feature "H3C2" (0.001836)
165. feature "KMT5B" (0.001835)
166. feature "APLP2" (0.001829)
167. feature "RPS28" (0.001828)
168. feature "SHOX" (0.001825)
169. feature "AHNAK2" (0.001823)
170. feature "TEDC2-AS1" (0.001812)
171. feature "XRCC1" (0.001811)
172. feature "TAF13" (0.001804)
173. feature "RPLP0P2" (0.001792)
174. feature "PCIF1" (0.001791)
175. feature "CFAP251" (0.001783)
176. feature "EHBP1L1" (0.001778)
177. feature "AKT1S1" (0.001771)
178. feature "S100A11" (0.001764)
179. feature "RPL17" (0.001755)
180. feature "MDC1" (0.001752)
181. feature "TSR1" (0.001743)
182. feature "ERO1A" (0.001741)
183. feature "GPI" (0.001731)
184. feature "CCNI" (0.001709)
185. feature "MT-ND3" (0.001702)
186. feature "ANP32B" (0.001683)
187. feature "SOS1" (0.001655)
188. feature "PAQR7" (0.001647)
189. feature "NCALD" (0.001635)
190. feature "H4C5" (0.001625)
191. feature "RAB4A" (0.001606)
192. feature "H2AC11" (0.001595)
193. feature "MT-TQ" (0.001593)
194. feature "KRT19" (0.001591)
195. feature "NCOR2" (0.001588)
196. feature "MT-ND1" (0.001584)
197. feature "SLC2A1" (0.001580)
198. feature "NEDD1" (0.001578)
199. feature "RANGAP1" (0.001578)
200. feature "H2AC12" (0.001574)
201. feature "C4orf3" (0.001561)
202. feature "BICD1" (0.001559)
203. feature "CDK19" (0.001554)
204. feature "PABPC1" (0.001552)
205. feature "MLLT6" (0.001543)
206. feature "KCNJ3" (0.001542)
207. feature "KRT10" (0.001542)
208. feature "LMNB2" (0.001538)
209. feature "H1-2" (0.001537)
210. feature "NCOR1" (0.001525)
211. feature "ST14" (0.001525)
212. feature "KIF3B" (0.001513)
213. feature "BRMS1" (0.001511)
214. feature "SYT14" (0.001501)
215. feature "CTSZ" (0.001497)
216. feature "CALM2" (0.001493)
217. feature "SCPEP1" (0.001491)
218. feature "GNB1" (0.001487)
219. feature "WIPF2" (0.001486)
220. feature "RPS14" (0.001470)
221. feature "LINC02511" (0.001464)
222. feature "ENO1" (0.001463)
223. feature "GAPDH" (0.001455)
224. feature "RRAS" (0.001441)
225. feature "HRAS" (0.001438)
226. feature "CAMSAP2" (0.001436)
227. feature "MARCKS" (0.001430)
228. feature "TMA7" (0.001410)
229. feature "AK1" (0.001405)
230. feature "SIVA1" (0.001405)
231. feature "WBP4" (0.001400)
232. feature "PLOD2" (0.001399)
233. feature "FAM200B" (0.001399)
234. feature "MALAT1" (0.001397)
235. feature "RPLP1" (0.001393)
236. feature "CDC42BPA" (0.001390)
237. feature "NACA4P" (0.001388)
238. feature "ZNF318" (0.001386)
239. feature "SAMD4B" (0.001384)
240. feature "IGFL2-AS1" (0.001383)
241. feature "MT-ND5" (0.001382)
242. feature "HMGN3" (0.001382)
243. feature "PSMA7" (0.001380)
244. feature "EPPK1" (0.001379)
245. feature "LCA5" (0.001377)
246. feature "XPNPEP1" (0.001369)
247. feature "KRT13" (0.001358)
248. feature "GUK1" (0.001353)
249. feature "RPL18A" (0.001348)
250. feature "CMAS" (0.001340)
251. feature "AIP" (0.001313)
252. feature "TK1" (0.001311)
253. feature "FLNA" (0.001297)
254. feature "PGAM1" (0.001297)
255. feature "CAST" (0.001296)
256. feature "MT2A" (0.001292)
257. feature "THEM4" (0.001282)
258. feature "MLLT10" (0.001277)
259. feature "GRK2" (0.001272)
260. feature "HSBP1" (0.001256)
261. feature "ANAPC16" (0.001247)
262. feature "PPP1R21" (0.001246)
263. feature "RPL27A" (0.001243)
264. feature "SLC25A48" (0.001234)
265. feature "H2AX" (0.001229)
266. feature "FABP5" (0.001216)
267. feature "HSPBP1" (0.001208)
268. feature "FOXP1" (0.001206)
269. feature "AP1M1" (0.001198)
270. feature "METRN" (0.001196)
271. feature "SEPTIN9" (0.001194)
272. feature "UBC" (0.001185)
273. feature "GPM6B" (0.001184)
274. feature "LAMB3" (0.001178)
275. feature "CNOT6L" (0.001169)
276. feature "CSTA" (0.001168)
277. feature "GTF3A" (0.001168)
278. feature "CAVIN1" (0.001159)
279. feature "BAP1" (0.001158)
280. feature "RPL36" (0.001158)
281. feature "PPIG" (0.001151)
282. feature "IFI27L2" (0.001144)
283. feature "BTN3A2" (0.001143)
284. feature "DCTN1" (0.001142)
285. feature "SERF2" (0.001137)
286. feature "LARP7" (0.001135)
287. feature "FTL" (0.001122)
288. feature "LMF2" (0.001120)
289. feature "NUDT1" (0.001105)
290. feature "CAP1" (0.001103)
291. feature "SENP7" (0.001101)
292. feature "OTULINL" (0.001096)
293. feature "CAMK2N1" (0.001094)
294. feature "RPL10" (0.001093)
295. feature "S100A6" (0.001090)
296. feature "KIF5B" (0.001089)
297. feature "ATP5MG" (0.001082)
298. feature "TPI1" (0.001081)
299. feature "PDIA3" (0.001072)
300. feature "NDUFB10" (0.001067)
301. feature "EEF1A1" (0.001066)
302. feature "ENTR1" (0.001062)
303. feature "PDLIM1" (0.001061)
304. feature "MT-RNR1" (0.001060)
305. feature "YBX3" (0.001055)
306. feature "NIN" (0.001052)
307. feature "ELOB" (0.001037)
308. feature "HSP90AB1" (0.001030)
309. feature "CD47" (0.001028)
310. feature "H1-1" (0.001017)
311. feature "CANT1" (0.001015)
312. feature "PRR34-AS1" (0.001014)
313. feature "RPL35" (0.001007)
314. feature "SMARCB1" (0.001004)
315. feature "TCEAL4" (0.001001)
316. feature "EEF2" (0.001000)
317. feature "BRD4" (0.000996)
318. feature "ATP5MC2" (0.000991)
319. feature "PKP4" (0.000983)
320. feature "GIPC1" (0.000977)
321. feature "PRRG3" (0.000972)
322. feature "OPTN" (0.000969)
323. feature "MYH9" (0.000962)
324. feature "TOMM5" (0.000959)
325. feature "FAM162A" (0.000952)
326. feature "CREB3L2" (0.000951)
327. feature "ACTG1" (0.000938)
328. feature "CARMIL1" (0.000937)
329. feature "UBN2" (0.000937)
330. feature "MZT2B" (0.000935)
331. feature "TMSB4X" (0.000934)
332. feature "CAV1" (0.000933)
333. feature "RPL28" (0.000927)
334. feature "RPL37" (0.000925)
335. feature "KCNJ2" (0.000923)
336. feature "CMIP" (0.000919)
337. feature "TMEM238" (0.000918)
338. feature "ZNF688" (0.000917)
339. feature "GOLGA5" (0.000914)
340. feature "TPX2" (0.000913)
341. feature "RPL27" (0.000907)
342. feature "MKI67" (0.000904)
343. feature "UQCC2" (0.000903)
344. feature "SNHG6" (0.000903)
345. feature "POLR2H" (0.000902)
346. feature "METTL26" (0.000898)
347. feature "CFAP36" (0.000898)
348. feature "DNAAF5" (0.000898)
349. feature "UQCRQ" (0.000894)
350. feature "NEDD4L" (0.000894)
351. feature "FUBP3" (0.000893)
352. feature "HMGB2" (0.000891)
353. feature "NEFL" (0.000890)
354. feature "MT-CO2" (0.000887)
355. feature "HSPA5" (0.000886)
356. feature "ATP1A1" (0.000881)
357. feature "ANXA1" (0.000881)
358. feature "RPSA" (0.000876)
359. feature "H1-3" (0.000876)
360. feature "GPX4" (0.000874)
361. feature "PPDPF" (0.000874)
362. feature "YWHAZ" (0.000874)
363. feature "TPT1" (0.000873)
364. feature "BRWD3" (0.000871)
365. feature "DNMT1" (0.000865)
366. feature "HSP90B1" (0.000862)
367. feature "STRN4" (0.000859)
368. feature "SIGMAR1" (0.000857)
369. feature "PTGR1" (0.000857)
370. feature "PRDM2" (0.000851)
371. feature "H3-3A" (0.000851)
372. feature "RAB8B" (0.000850)
373. feature "MTA2" (0.000847)
374. feature "GBP1P1" (0.000846)
375. feature "MT-RNR2" (0.000843)
376. feature "LBH" (0.000839)
377. feature "FOSL2" (0.000833)
378. feature "MAFF" (0.000830)
379. feature "FCHO2" (0.000827)
380. feature "MSANTD3" (0.000826)
381. feature "UBE2I" (0.000825)
382. feature "CALR" (0.000824)
383. feature "PFN1" (0.000821)
384. feature "RTF1" (0.000820)
385. feature "C19orf53" (0.000819)
386. feature "KRT7" (0.000818)
387. feature "ATP5F1E" (0.000814)
388. feature "STMN1" (0.000808)
389. feature "CDK2AP1" (0.000807)
390. feature "CENPF" (0.000795)
391. feature "YTHDC1" (0.000793)
392. feature "MTND1P23" (0.000789)
393. feature "PAQR8" (0.000789)
394. feature "DTYMK" (0.000777)
395. feature "RNF10" (0.000777)
396. feature "TMEM160" (0.000777)
397. feature "RPL35A" (0.000776)
398. feature "MT-CO3" (0.000775)
399. feature "GNAI2" (0.000774)
400. feature "RPS6KA6" (0.000773)
401. feature "NMT2" (0.000772)
402. feature "SCNM1" (0.000772)
403. feature "RPS19" (0.000770)
404. feature "TAOK3" (0.000764)
405. feature "RPL12" (0.000763)
406. feature "LINC01304" (0.000756)
407. feature "PPP1R14B" (0.000754)
408. feature "PLEKHG3" (0.000750)
409. feature "OSBPL3" (0.000744)
410. feature "COPS9" (0.000743)
411. feature "RPS5" (0.000742)
412. feature "PKM" (0.000737)
413. feature "HCFC1R1" (0.000728)
414. feature "POLE4" (0.000727)
415. feature "ATAD2" (0.000719)
416. feature "ZHX1" (0.000719)
417. feature "RPS2" (0.000718)
418. feature "OAZ1" (0.000710)
419. feature "PTMS" (0.000699)
420. feature "S100A2" (0.000698)
421. feature "MAP7" (0.000698)
422. feature "PSMG1" (0.000696)
423. feature "DGKD" (0.000695)
424. feature "KDM1A" (0.000694)
425. feature "PDIA6" (0.000693)
426. feature "RASGEF1B" (0.000692)
427. feature "ADORA2B" (0.000692)
428. feature "ANP32A" (0.000686)
429. feature "DSP" (0.000682)
430. feature "NCAM1" (0.000682)
431. feature "TNS4" (0.000672)
432. feature "PHGDH" (0.000668)
433. feature "HDGF" (0.000667)
434. feature "CHCHD7" (0.000665)
435. feature "CBX3" (0.000664)
436. feature "KRT17" (0.000664)
437. feature "PRR11" (0.000657)
438. feature "NFE2L2" (0.000655)
439. feature "SPRYD4" (0.000654)
440. feature "TRIM44" (0.000650)
441. feature "UNK" (0.000649)
442. feature "RPS15" (0.000647)
443. feature "UBA3" (0.000646)
444. feature "POLR2L" (0.000646)
445. feature "SOX4" (0.000644)
446. feature "RAC1" (0.000642)
447. feature "SNRPA" (0.000639)
448. feature "RPL37A" (0.000636)
449. feature "DEK" (0.000634)
450. feature "RPL21" (0.000634)
451. feature "RPL18" (0.000631)
452. feature "LPAR4" (0.000631)
453. feature "RPL13" (0.000621)
454. feature "PFKP" (0.000620)
455. feature "EFNB2" (0.000619)
456. feature "HNRNPM" (0.000617)
457. feature "BIN1" (0.000614)
458. feature "H4C3" (0.000613)
459. feature "HSPB1" (0.000612)
460. feature "EZH1" (0.000610)
461. feature "RPS12" (0.000604)
462. feature "AHNAK" (0.000599)
463. feature "H2AZ2" (0.000598)
464. feature "MT-ND4L" (0.000596)
465. feature "MYL12B" (0.000592)
466. feature "YBX1" (0.000592)
467. feature "HNRNPA2B1" (0.000587)
468. feature "SRI" (0.000587)
469. feature "ARAP1" (0.000583)
470. feature "MCM4" (0.000582)
471. feature "NDUFB2" (0.000582)
472. feature "KRT14" (0.000582)
473. feature "RPLP2" (0.000578)
474. feature "GSTP1" (0.000576)
475. feature "RPS29" (0.000575)
476. feature "SGO2" (0.000574)
477. feature "CSTB" (0.000571)
478. feature "GOLGA3" (0.000570)
479. feature "CYB561D1" (0.000569)
480. feature "BACE2" (0.000568)
481. feature "EXOC7" (0.000565)
482. feature "NAB1" (0.000559)
483. feature "H1-4" (0.000559)
484. feature "PRMT2" (0.000555)
485. feature "MPHOSPH6" (0.000553)
486. feature "TANGO2" (0.000553)
487. feature "MT-ND4" (0.000551)
488. feature "USP53" (0.000548)
489. feature "LRRC59" (0.000547)
490. feature "CNTROB" (0.000545)
491. feature "GOLGA4" (0.000544)
492. feature "UQCR11" (0.000540)
493. feature "HNRNPU" (0.000530)
494. feature "EPB41L1" (0.000525)
495. feature "MINK1" (0.000521)
496. feature "PRR5L" (0.000519)
497. feature "RPS8" (0.000519)
498. feature "NMD3" (0.000512)
499. feature "CNR2" (0.000511)
500. feature "RAP2B" (0.000510)
501. feature "ZCCHC9" (0.000507)
502. feature "ODF2" (0.000504)
503. feature "CCDC34" (0.000504)
504. feature "FBXL19" (0.000504)
505. feature "HES1" (0.000499)
506. feature "HSPA4" (0.000495)
507. feature "NRBP1" (0.000493)
508. feature "TAGLN2" (0.000490)
509. feature "GNG5" (0.000488)
510. feature "SCRIB" (0.000485)
511. feature "TRPV4" (0.000481)
512. feature "FAM107B" (0.000478)
513. feature "SHISA5" (0.000478)
514. feature "DLG5" (0.000474)
515. feature "JUN" (0.000473)
516. feature "MZT2A" (0.000473)
517. feature "DPCD" (0.000471)
518. feature "SFN" (0.000468)
519. feature "EZR" (0.000466)
520. feature "CIRBP" (0.000465)
521. feature "PUS7" (0.000464)
522. feature "HCFC1" (0.000460)
523. feature "PAIP2" (0.000445)
524. feature "CDK14" (0.000431)
525. feature "MAPK12" (0.000424)
526. feature "KRT5" (0.000420)
527. feature "UNC45A" (0.000418)
528. feature "RPL22L1" (0.000418)
529. feature "KAT7" (0.000413)
530. feature "RPL8" (0.000412)
531. feature "CCNY" (0.000410)
532. feature "MRPS33" (0.000410)
533. feature "SNRNP70" (0.000409)
534. feature "NARF" (0.000408)
535. feature "ATP2A2" (0.000405)
536. feature "SRC" (0.000402)
537. feature "UBE2C" (0.000401)
538. feature "TTC19" (0.000400)
539. feature "GSK3A" (0.000397)
540. feature "LRRC20" (0.000392)
541. feature "H1-5" (0.000392)
542. feature "H2AW" (0.000388)
543. feature "NDUFC1" (0.000387)
544. feature "MT-ND2" (0.000386)
545. feature "CRPPA" (0.000385)
546. feature "LRRFIP2" (0.000385)
547. feature "TMEM70" (0.000383)
548. feature "DMD" (0.000378)
549. feature "ZRANB1" (0.000376)
550. feature "RPS21" (0.000373)
551. feature "VPS45" (0.000372)
552. feature "CDKN3" (0.000370)
553. feature "MT-ATP6" (0.000370)
554. feature "LRRIQ1" (0.000369)
555. feature "DEPDC1B" (0.000365)
556. feature "ZFHX3" (0.000360)
557. feature "SH3RF1" (0.000358)
558. feature "PSIP1" (0.000354)
559. feature "PPP2R1A" (0.000353)
560. feature "DDX54" (0.000352)
561. feature "ZMYND8" (0.000352)
562. feature "INHBA" (0.000350)
563. feature "MRPL33" (0.000350)
564. feature "TOLLIP" (0.000342)
565. feature "UACA" (0.000337)
566. feature "MARCHF6" (0.000330)
567. feature "ATOX1" (0.000320)
568. feature "SLC25A37" (0.000320)
569. feature "ZNF148" (0.000311)
570. feature "BAHD1" (0.000308)
571. feature "TUBB6" (0.000303)
572. feature "EGR1" (0.000300)
573. feature "CDK2AP2" (0.000300)
574. feature "IPO9" (0.000296)
575. feature "JUND" (0.000275)
576. feature "EHD2" (0.000270)
577. feature "MRPL55" (0.000257)
578. feature "ATP5ME" (0.000257)
579. feature "MARVELD1" (0.000252)
580. feature "RAD54L2" (0.000246)
581. feature "TFE3" (0.000243)
582. feature "RHOA" (0.000243)
583. feature "RBAK" (0.000241)
584. feature "TSPO" (0.000239)
585. feature "COMMD6" (0.000237)
586. feature "MDFI" (0.000235)
587. feature "KIF9" (0.000231)
588. feature "LEO1" (0.000216)
589. feature "HOXB7" (0.000216)
590. feature "ZBED4" (0.000205)
591. feature "TWNK" (0.000199)
592. feature "NDUFB1" (0.000176)
593. feature "CAPZB" (0.000167)
594. feature "NR2F6" (0.000159)
595. feature "POLR2A" (0.000159)
596. feature "SAMD4A" (0.000145)
597. feature "IMP3" (0.000118)
598. feature "FAM83A" (0.000106)
599. feature "UIMC1" (0.000104)
600. feature "LAGE3" (0.000103)
601. feature "TCEAL9" (0.000102)
602. feature "DOHH" (0.000099)
603. feature "BRCC3" (0.000099)
604. feature "KHNYN" (0.000094)
605. feature "PPP1R13L" (0.000079)
606. feature "BMPR1B" (0.000076)
607. feature "VAT1" (0.000062)
608. feature "PHLDB2" (0.000052)
609. feature "IRAK1" (0.000049)
610. feature "TF" (0.000025)
611. feature "MAP3K4" (0.000025)
612. feature "NIBAN2" (0.000010)
613. feature "TMEM141" (0.000003)
614. feature "FGFR1OP2" (0.000001)
615. feature "POLR3GL" (0.000000)
616. feature "MZT1" (0.000000)
617. feature "ALS2CL" (0.000000)
618. feature "FBXL17" (0.000000)
619. feature "MUC4" (0.000000)
620. feature "BTBD6" (0.000000)
621. feature "SORBS3" (0.000000)
622. feature "HERPUD2-AS1" (0.000000)
623. feature "NLE1" (0.000000)
624. feature "CCDC62" (0.000000)
625. feature "ZNF76" (0.000000)
626. feature "ARMH3" (0.000000)
627. feature "RHOB" (0.000000)
628. feature "CHIC2" (0.000000)
629. feature "P2RX7" (0.000000)
630. feature "SREBF1" (0.000000)
631. feature "KLHDC3" (0.000000)
632. feature "IQANK1" (0.000000)
633. feature "SREK1IP1P1" (0.000000)
634. feature "RAB13" (0.000000)
635. feature "MTND2P28" (0.000000)
636. feature "HNF4A" (0.000000)
637. feature "RNF170" (0.000000)
638. feature "SLC40A1" (0.000000)
639. feature "MMAA" (0.000000)
640. feature "SDAD1P1" (0.000000)
641. feature "MIR663AHG" (0.000000)
642. feature "ITM2C" (0.000000)
643. feature "WWC1" (0.000000)
644. feature "NCOA1" (0.000000)
645. feature "OSER1-DT" (0.000000)
646. feature "BLCAP" (0.000000)
647. feature "TLE2" (0.000000)
648. feature "RAB42" (0.000000)
649. feature "TNFRSF12A" (0.000000)
650. feature "ZNF132" (0.000000)
651. feature "PPP2R3B" (0.000000)
652. feature "WAC-AS1" (0.000000)
653. feature "C16orf95-DT" (0.000000)
654. feature "ENKD1" (0.000000)
655. feature "CACNB3" (0.000000)
656. feature "ABT1" (0.000000)
657. feature "ZBTB33" (0.000000)
658. feature "ZNF280B" (0.000000)
659. feature "TCAIM" (0.000000)
660. feature "SMIM4" (0.000000)
661. feature "FAM86C2P" (0.000000)
662. feature "RALGDS" (0.000000)
663. feature "NRARP" (0.000000)
664. feature "HAGLROS" (0.000000)
665. feature "CST6" (0.000000)
666. feature "CDR2" (0.000000)
667. feature "SNX11" (0.000000)
668. feature "RPS15AP29" (0.000000)
669. feature "ARHGEF17" (0.000000)
670. feature "PRXL2B" (0.000000)
671. feature "IKBKE" (0.000000)
672. feature "MTCO1P12" (0.000000)
673. feature "PALM3" (0.000000)
674. feature "DANT2" (0.000000)
675. feature "THNSL1" (0.000000)
676. feature "TRAIP" (0.000000)
677. feature "RBM43" (0.000000)
678. feature "HES4" (0.000000)
679. feature "IFIT1" (0.000000)
680. feature "BOK" (0.000000)
681. feature "CRTAM" (0.000000)
682. feature "SMDT1" (0.000000)
683. feature "PITPNM1" (0.000000)
684. feature "SMOX" (0.000000)
685. feature "NOTCH2NLC" (0.000000)
686. feature "C12orf60" (0.000000)
687. feature "NEK1" (0.000000)
688. feature "AGBL5" (0.000000)
689. feature "TINF2" (0.000000)
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2720. feature "KLLN" (0.000000)
2721. feature "NLRP8" (0.000000)
2722. feature "TRIM7" (0.000000)
2723. feature "HHLA2" (0.000000)
2724. feature "MXI1" (0.000000)
2725. feature "FUT11" (0.000000)
2726. feature "LMX1B" (0.000000)
2727. feature "MYOM3" (0.000000)
2728. feature "DNAAF9" (0.000000)
2729. feature "TDO2" (0.000000)
2730. feature "VPS54" (0.000000)
2731. feature "STK11" (0.000000)
2732. feature "VAV2" (0.000000)
2733. feature "ESR1" (0.000000)
2734. feature "TMEM44-AS1" (0.000000)
2735. feature "KXD1" (0.000000)
2736. feature "HDDC3" (0.000000)
2737. feature "EFCAB1" (0.000000)
2738. feature "BMP1" (0.000000)
2739. feature "FLOT1" (0.000000)
2740. feature "SIX2" (0.000000)
2741. feature "HBA1" (0.000000)
2742. feature "HNRNPA1" (0.000000)
2743. feature "C18orf32" (0.000000)
2744. feature "AKAP6" (0.000000)
2745. feature "CUL9" (0.000000)
2746. feature "PIH1D2" (0.000000)
2747. feature "PAPSS2" (0.000000)
2748. feature "HIGD1A" (0.000000)
2749. feature "RNF25" (0.000000)
2750. feature "NCK2" (0.000000)
2751. feature "SMCO4" (0.000000)
2752. feature "HECA" (0.000000)
2753. feature "SNHG18" (0.000000)
2754. feature "TMEM25" (0.000000)
2755. feature "FHL3" (0.000000)
2756. feature "LERFS" (0.000000)
2757. feature "MED18" (0.000000)
2758. feature "CYP2C9" (0.000000)
2759. feature "TMEM40" (0.000000)
2760. feature "CEP83" (0.000000)
2761. feature "LZTR1" (0.000000)
2762. feature "ARF3" (0.000000)
2763. feature "CCDC136" (0.000000)
2764. feature "VAMP7" (0.000000)
2765. feature "IRS1" (0.000000)
2766. feature "DGUOK" (0.000000)
2767. feature "SRA1" (0.000000)
2768. feature "TKFC" (0.000000)
2769. feature "MAPK14" (0.000000)
2770. feature "SNCG" (0.000000)
2771. feature "RELA" (0.000000)
2772. feature "ZBTB46" (0.000000)
2773. feature "WDR35" (0.000000)
2774. feature "MAP3K13" (0.000000)
2775. feature "GDE1" (0.000000)
2776. feature "FTHL17" (0.000000)
2777. feature "RPA1" (0.000000)
2778. feature "MED29" (0.000000)
2779. feature "CCDC153" (0.000000)
2780. feature "GNL1" (0.000000)
2781. feature "TMPOP2" (0.000000)
2782. feature "KCNJ15" (0.000000)
2783. feature "SLC25A12" (0.000000)
2784. feature "ARMC6" (0.000000)
2785. feature "CLDN15" (0.000000)
2786. feature "SLC30A10" (0.000000)
2787. feature "RNF187" (0.000000)
2788. feature "DLGAP1-AS2" (0.000000)
2789. feature "BET1" (0.000000)
2790. feature "LINC-PINT" (0.000000)
2791. feature "CKS1B" (0.000000)
2792. feature "EID1" (0.000000)
2793. feature "FOXO3" (0.000000)
2794. feature "TBC1D7" (0.000000)
2795. feature "ZNF571-AS1" (0.000000)
2796. feature "DCAF6" (0.000000)
2797. feature "LINC02178" (0.000000)
2798. feature "PRDM11" (0.000000)
2799. feature "SYK" (0.000000)
2800. feature "CCS" (0.000000)
2801. feature "SLC31A2" (0.000000)
2802. feature "SLC25A1" (0.000000)
2803. feature "CUEDC2" (0.000000)
2804. feature "VASH2" (0.000000)
2805. feature "SEMA3D" (0.000000)
2806. feature "C11orf98" (0.000000)
2807. feature "LIMS1" (0.000000)
2808. feature "TJAP1" (0.000000)
2809. feature "POLR3A" (0.000000)
2810. feature "NOC2LP1" (0.000000)
2811. feature "NUP62CL" (0.000000)
2812. feature "RCHY1" (0.000000)
2813. feature "TRAPPC2B" (0.000000)
2814. feature "ALX1" (0.000000)
2815. feature "SF3B4" (0.000000)
2816. feature "MTFP1" (0.000000)
2817. feature "SNX18" (0.000000)
2818. feature "ABTB1" (0.000000)
2819. feature "PBXIP1" (0.000000)
2820. feature "TBCCD1" (0.000000)
2821. feature "PIP4P2" (0.000000)
2822. feature "DUSP12" (0.000000)
2823. feature "WDR53" (0.000000)
2824. feature "CLMN" (0.000000)
2825. feature "LEMD3" (0.000000)
2826. feature "ADPRM" (0.000000)
2827. feature "GYS1" (0.000000)
2828. feature "SNAPC1" (0.000000)
2829. feature "LINC01503" (0.000000)
2830. feature "LARGE1" (0.000000)
2831. feature "PSD3" (0.000000)
2832. feature "LINC02894" (0.000000)
2833. feature "MAP3K12" (0.000000)
2834. feature "HIP1R" (0.000000)
2835. feature "RAB35" (0.000000)
2836. feature "OCRL" (0.000000)
2837. feature "RNLS" (0.000000)
2838. feature "PHRF1" (0.000000)
2839. feature "RNF215" (0.000000)
2840. feature "HAGLR" (0.000000)
2841. feature "LAMA5" (0.000000)
2842. feature "ZG16B" (0.000000)
2843. feature "SLC52A1" (0.000000)
2844. feature "CELSR1" (0.000000)
2845. feature "IMMP2L" (0.000000)
2846. feature "ADM" (0.000000)
2847. feature "IRF2BPL" (0.000000)
2848. feature "TSHZ2" (0.000000)
2849. feature "PBX4" (0.000000)
2850. feature "ARL9" (0.000000)
2851. feature "BMPR2" (0.000000)
2852. feature "WWTR1-AS1" (0.000000)
2853. feature "ZNF628" (0.000000)
2854. feature "LINC01962" (0.000000)
2855. feature "MOV10" (0.000000)
2856. feature "ARHGEF39" (0.000000)
2857. feature "NKX3-1" (0.000000)
2858. feature "JMJD6" (0.000000)
2859. feature "MSH5" (0.000000)
2860. feature "ABHD12B" (0.000000)
2861. feature "ACVR1" (0.000000)
2862. feature "ZNF99" (0.000000)
2863. feature "MEX3D" (0.000000)
2864. feature "ADNP-AS1" (0.000000)
2865. feature "AKAP5" (0.000000)
2866. feature "RRAGA" (0.000000)
2867. feature "TSNARE1" (0.000000)
2868. feature "CHMP7" (0.000000)
2869. feature "ZNF784" (0.000000)
2870. feature "CEACAM19" (0.000000)
2871. feature "FAM214B" (0.000000)
2872. feature "CTBP1" (0.000000)
2873. feature "PDE9A" (0.000000)
2874. feature "ZNF826P" (0.000000)
2875. feature "SUSD5" (0.000000)
2876. feature "BAMBI" (0.000000)
2877. feature "NUDT8" (0.000000)
2878. feature "APLF" (0.000000)
2879. feature "TCP11L1" (0.000000)
2880. feature "CBX6" (0.000000)
2881. feature "SLC8A1" (0.000000)
2882. feature "ITGA3" (0.000000)
2883. feature "TCF15" (0.000000)
2884. feature "LINC02057" (0.000000)
2885. feature "ATF3" (0.000000)
2886. feature "MMP2" (0.000000)
2887. feature "ITSN1" (0.000000)
2888. feature "FLAD1" (0.000000)
2889. feature "P2RY2" (0.000000)
2890. feature "ALDOC" (0.000000)
2891. feature "NLRX1" (0.000000)
2892. feature "ZNF446" (0.000000)
2893. feature "KIF16B" (0.000000)
2894. feature "DOC2A" (0.000000)
2895. feature "CENPBD1" (0.000000)
2896. feature "CDK9" (0.000000)
2897. feature "RAVER1" (0.000000)
2898. feature "MSRB3" (0.000000)
2899. feature "PLD5" (0.000000)
2900. feature "RNF19B" (0.000000)
2901. feature "CBWD3" (0.000000)
2902. feature "NEBL" (0.000000)
2903. feature "TBC1D22A" (0.000000)
2904. feature "GNG4" (0.000000)
2905. feature "IL12B" (0.000000)
2906. feature "B4GALT2" (0.000000)
2907. feature "MT-TH" (0.000000)
2908. feature "ZNF469" (0.000000)
2909. feature "ITPKC" (0.000000)
2910. feature "MYH14" (0.000000)
2911. feature "DOCK9" (0.000000)
2912. feature "CDH24" (0.000000)
2913. feature "DCAF10" (0.000000)
2914. feature "CCDC168" (0.000000)
2915. feature "LINC02672" (0.000000)
2916. feature "C19orf81" (0.000000)
2917. feature "MBD1" (0.000000)
2918. feature "CDPF1" (0.000000)
2919. feature "KHK" (0.000000)
2920. feature "TCF3" (0.000000)
2921. feature "GRIN2D" (0.000000)
2922. feature "IFNGR2" (0.000000)
2923. feature "ATP6V1B2" (0.000000)
2924. feature "SPINK5" (0.000000)
2925. feature "FAM13B" (0.000000)
2926. feature "CAPNS1" (0.000000)
2927. feature "TAF10" (0.000000)
2928. feature "S1PR3" (0.000000)
2929. feature "HOXA11-AS" (0.000000)
2930. feature "MIR663B" (0.000000)
2931. feature "LINC00911" (0.000000)
2932. feature "YBEY" (0.000000)
2933. feature "ZNF268" (0.000000)
2934. feature "MORC3" (0.000000)
2935. feature "RNASEH1-AS1" (0.000000)
2936. feature "SLC35B3" (0.000000)
2937. feature "PFKFB3" (0.000000)
2938. feature "MXRA5" (0.000000)
2939. feature "ETAA1" (0.000000)
2940. feature "ZYG11A" (0.000000)
2941. feature "PPTC7" (0.000000)
2942. feature "FRAT2" (0.000000)
2943. feature "SDSL" (0.000000)
2944. feature "NUMA1" (0.000000)
2945. feature "C19orf54" (0.000000)
2946. feature "KDM3A" (0.000000)
2947. feature "KIRREL1" (0.000000)
2948. feature "SLC9A3" (0.000000)
2949. feature "ZNF33B" (0.000000)
2950. feature "RN7SL471P" (0.000000)
2951. feature "DPRX" (0.000000)
2952. feature "C17orf99" (0.000000)
2953. feature "PIK3R2" (0.000000)
2954. feature "ARHGEF28" (0.000000)
2955. feature "PEX11A" (0.000000)
2956. feature "OXNAD1" (0.000000)
2957. feature "TONSL" (0.000000)
2958. feature "HERC1" (0.000000)
2959. feature "ACVR2B" (0.000000)
2960. feature "FHDC1" (0.000000)
2961. feature "ZBTB47" (0.000000)
2962. feature "MAML1" (0.000000)
2963. feature "PCDHGA10" (0.000000)
2964. feature "IL18" (0.000000)
2965. feature "RGS10" (0.000000)
2966. feature "S100A4" (0.000000)
2967. feature "DOP1B" (0.000000)
2968. feature "TRMT5" (0.000000)
2969. feature "PATZ1" (0.000000)
2970. feature "MON1B" (0.000000)
2971. feature "LTBP4" (0.000000)
2972. feature "TNFAIP8L1" (0.000000)
2973. feature "ZBTB38" (0.000000)
2974. feature "OVCH1-AS1" (0.000000)
2975. feature "IL6R" (0.000000)
2976. feature "PXMP4" (0.000000)
2977. feature "KCNMB4" (0.000000)
2978. feature "DHX35-DT" (0.000000)
2979. feature "MAGEA4" (0.000000)
2980. feature "PTCH1" (0.000000)
2981. feature "TGDS" (0.000000)
2982. feature "MAGI2-AS3" (0.000000)
2983. feature "PPIH" (0.000000)
2984. feature "VPS26B" (0.000000)
2985. feature "FKBP9P1" (0.000000)
2986. feature "WDR18" (0.000000)
2987. feature "CAVIN2" (0.000000)
2988. feature "IDH3G" (0.000000)
2989. feature "NEUROD2" (0.000000)
2990. feature "PKN1" (0.000000)
2991. feature "RHBDL2" (0.000000)
2992. feature "C16orf74" (0.000000)
2993. feature "MCRIP1" (0.000000)
2994. feature "GJD2" (0.000000)
2995. feature "HMBOX1" (0.000000)
2996. feature "CTU2" (0.000000)
2997. feature "CCDC191" (0.000000)
2998. feature "EPHX1" (0.000000)
2999. feature "GREP1" (0.000000)
3000. feature "TROAP" (0.000000)

¶

4.4.4) Neural Network

We now define the neural network model to be used with the dataset dropseq HCC1806

In [ ]:
# This is the model (neural network) applied to the dataset dropseq HCC1806
model_DS_H = NeuralNetworkDropSeq()
model_DS_H
Out[ ]:
NeuralNetworkDropSeq(
  (model): Sequential(
    (0): Flatten(start_dim=1, end_dim=-1)
    (1): Linear(in_features=3000, out_features=512, bias=True)
    (2): LeakyReLU(negative_slope=0.01)
    (3): Dropout(p=0.4, inplace=False)
    (4): Linear(in_features=512, out_features=256, bias=True)
    (5): LeakyReLU(negative_slope=0.01)
    (6): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (7): Dropout(p=0.4, inplace=False)
    (8): Linear(in_features=256, out_features=128, bias=True)
    (9): LeakyReLU(negative_slope=0.01)
    (10): Dropout(p=0.3, inplace=False)
    (11): Linear(in_features=128, out_features=64, bias=True)
    (12): LeakyReLU(negative_slope=0.01)
    (13): Linear(in_features=64, out_features=1, bias=True)
  )
)

As we did for the MCF7 dataset, also here we split the train dataset and the labels into train dataset and validation dataset (this second one used to validate and see the accuracy during the training phase). Then we convert these variables into tensors in order to be able to use them with pytorch.

In [ ]:
# Convert the split datasets into tensors

X_train_DS_HCC1806_nn, X_val_DS_HCC1806_nn, y_train_DS_HCC1806_nn, y_val_DS_HCC1806_nn = train_test_split(X_train_df_DS_HCC1806.values, y_train_df_DS_HCC1806.values, test_size=0.2, random_state=42)


X_train_tensor_H = torch.tensor(X_train_DS_HCC1806_nn, dtype=torch.float32)
y_train_tensor_H = torch.tensor(y_train_DS_HCC1806_nn, dtype=torch.float32).unsqueeze(1)
X_val_tensor_H = torch.tensor(X_val_DS_HCC1806_nn, dtype=torch.float32)
y_val_tensor_H = torch.tensor(y_val_DS_HCC1806_nn, dtype=torch.float32).unsqueeze(1)  # For binary classification
¶

Training for the model drop-seq HCC1806

Here below there is the training process of the neural network using the dataset HCC1806 dropseq.

It follows the same structure of the training process used with the MCF7 dataset; therefore we use Stochastic Gradient Descent (SGD) as the optimizer with a learning rate of 0.01, and as a loss function the binary cross-entropy with logits ('BCEWithLogitsLoss'). Also here, the training runs for 1000 epochs.

The training loop starts by setting the model to training mode, resetting the gradients, performing a forward pass on the training data, calculating the training loss, and then using backpropagation to compute the gradients. The optimizer updates the model parameters based on these gradients. Finally, training accuracy is calculated by rounding the sigmoid-activated predictions and comparing them to the true labels.

For validation, the model is set to evaluation mode, and a forward pass is performed on the validation data. The validation loss and accuracy are calculated similarly to the training process but without computing gradients.

In [ ]:
optimizer = torch.optim.SGD(model_DS_H.parameters(), lr=0.01)
criterion = nn.BCEWithLogitsLoss() # applies a sigmoid activation to the model's raw logits and calculates the binary cross-entropy loss

epochs = 1000
best_val_acc = 0
patience = 3
no_improve_epochs = 0

best_model_path_H = 'best_model_final_H.pth'

for epoch in range(epochs):
    model_DS_H.train()
    optimizer.zero_grad() # resets the gradients of all model parameters to zero.
    train_output = model_DS_H(X_train_tensor_H)  # Forward pass with all training data, computes the model's predictions (output) for the entire training dataset
    train_loss = criterion(train_output, y_train_tensor_H) # calculates the difference between predicted outputs and actual labels
    train_loss.backward() #computes the gradients of the loss with respect to the model's parameters
    # This uses backpropagation to distribute the error back through the network
    optimizer.step() #The optimizer applies the selected algorithm (like SGD) to update the weights
    
    # Calculate training accuracy
    with torch.no_grad():
        train_predictions = torch.round(torch.sigmoid(train_output))
        train_correct = (train_predictions == y_train_tensor_H).sum().item()
        train_total = y_train_tensor_H.size(0)
        train_acc = train_correct / train_total

    # Validation
    model_DS_H.eval() #  sets the model to evaluation mode, disabling features like dropout and batch normalization to ensure consistency
    with torch.no_grad(): #prevents PyTorch from computing gradients during evaluation.
        val_output = model_DS_H(X_val_tensor_H) #computes predictions on the validation dataset
        val_loss = criterion(val_output, y_val_tensor_H)
        val_predictions = torch.round(torch.sigmoid(val_output))  # converts the logits/predictions into binary predictions (0 or 1)
        val_correct = (val_predictions == y_val_tensor_H).sum().item()
        val_total = y_val_tensor_H.size(0)
        val_acc = val_correct / val_total

    print(f'Epoch {epoch+1}/{epochs}, '
          f'Train Loss: {train_loss.item():.4f}, '
          f'Train Accuracy: {train_acc:.4f}, '
          f'Val Loss: {val_loss.item():.4f}, '
          f'Val Accuracy: {val_acc:.4f}')
    
    

# Save model after all epochs are completed
#torch.save(model.state_dict(), best_model_path)   

Epoch 1/1000, Train Loss: 0.7117, Train Accuracy: 0.4291, Val Loss: 0.7004, Val Accuracy: 0.4110
Epoch 2/1000, Train Loss: 0.7104, Train Accuracy: 0.4312, Val Loss: 0.7002, Val Accuracy: 0.4110
Epoch 3/1000, Train Loss: 0.7091, Train Accuracy: 0.4357, Val Loss: 0.6999, Val Accuracy: 0.4110
Epoch 4/1000, Train Loss: 0.7065, Train Accuracy: 0.4475, Val Loss: 0.6997, Val Accuracy: 0.4110
Epoch 5/1000, Train Loss: 0.7058, Train Accuracy: 0.4430, Val Loss: 0.6995, Val Accuracy: 0.4110
Epoch 6/1000, Train Loss: 0.7060, Train Accuracy: 0.4469, Val Loss: 0.6992, Val Accuracy: 0.4110
Epoch 7/1000, Train Loss: 0.7020, Train Accuracy: 0.4620, Val Loss: 0.6990, Val Accuracy: 0.4110
Epoch 8/1000, Train Loss: 0.7011, Train Accuracy: 0.4722, Val Loss: 0.6988, Val Accuracy: 0.4110
Epoch 9/1000, Train Loss: 0.6997, Train Accuracy: 0.4728, Val Loss: 0.6985, Val Accuracy: 0.4110
Epoch 10/1000, Train Loss: 0.6980, Train Accuracy: 0.4814, Val Loss: 0.6983, Val Accuracy: 0.4110
Epoch 11/1000, Train Loss: 0.6983, Train Accuracy: 0.4841, Val Loss: 0.6980, Val Accuracy: 0.4110
Epoch 12/1000, Train Loss: 0.6972, Train Accuracy: 0.4852, Val Loss: 0.6978, Val Accuracy: 0.4110
Epoch 13/1000, Train Loss: 0.6961, Train Accuracy: 0.4922, Val Loss: 0.6975, Val Accuracy: 0.4110
Epoch 14/1000, Train Loss: 0.6945, Train Accuracy: 0.5033, Val Loss: 0.6973, Val Accuracy: 0.4110
Epoch 15/1000, Train Loss: 0.6932, Train Accuracy: 0.5144, Val Loss: 0.6970, Val Accuracy: 0.4110
Epoch 16/1000, Train Loss: 0.6924, Train Accuracy: 0.5102, Val Loss: 0.6967, Val Accuracy: 0.4110
Epoch 17/1000, Train Loss: 0.6899, Train Accuracy: 0.5273, Val Loss: 0.6965, Val Accuracy: 0.4110
Epoch 18/1000, Train Loss: 0.6903, Train Accuracy: 0.5323, Val Loss: 0.6962, Val Accuracy: 0.4110
Epoch 19/1000, Train Loss: 0.6890, Train Accuracy: 0.5434, Val Loss: 0.6959, Val Accuracy: 0.4110
Epoch 20/1000, Train Loss: 0.6891, Train Accuracy: 0.5379, Val Loss: 0.6956, Val Accuracy: 0.4110
Epoch 21/1000, Train Loss: 0.6874, Train Accuracy: 0.5403, Val Loss: 0.6953, Val Accuracy: 0.4110
Epoch 22/1000, Train Loss: 0.6862, Train Accuracy: 0.5580, Val Loss: 0.6949, Val Accuracy: 0.4110
Epoch 23/1000, Train Loss: 0.6841, Train Accuracy: 0.5734, Val Loss: 0.6946, Val Accuracy: 0.4110
Epoch 24/1000, Train Loss: 0.6834, Train Accuracy: 0.5746, Val Loss: 0.6942, Val Accuracy: 0.4115
Epoch 25/1000, Train Loss: 0.6828, Train Accuracy: 0.5738, Val Loss: 0.6939, Val Accuracy: 0.4115
Epoch 26/1000, Train Loss: 0.6809, Train Accuracy: 0.5900, Val Loss: 0.6935, Val Accuracy: 0.4163
Epoch 27/1000, Train Loss: 0.6806, Train Accuracy: 0.5913, Val Loss: 0.6931, Val Accuracy: 0.4246
Epoch 28/1000, Train Loss: 0.6781, Train Accuracy: 0.6013, Val Loss: 0.6927, Val Accuracy: 0.4334
Epoch 29/1000, Train Loss: 0.6782, Train Accuracy: 0.6039, Val Loss: 0.6923, Val Accuracy: 0.4548
Epoch 30/1000, Train Loss: 0.6777, Train Accuracy: 0.6061, Val Loss: 0.6918, Val Accuracy: 0.4669
Epoch 31/1000, Train Loss: 0.6759, Train Accuracy: 0.6176, Val Loss: 0.6913, Val Accuracy: 0.4864
Epoch 32/1000, Train Loss: 0.6749, Train Accuracy: 0.6196, Val Loss: 0.6908, Val Accuracy: 0.5088
Epoch 33/1000, Train Loss: 0.6746, Train Accuracy: 0.6232, Val Loss: 0.6903, Val Accuracy: 0.5370
Epoch 34/1000, Train Loss: 0.6732, Train Accuracy: 0.6285, Val Loss: 0.6898, Val Accuracy: 0.5705
Epoch 35/1000, Train Loss: 0.6722, Train Accuracy: 0.6305, Val Loss: 0.6892, Val Accuracy: 0.5948
Epoch 36/1000, Train Loss: 0.6704, Train Accuracy: 0.6448, Val Loss: 0.6886, Val Accuracy: 0.6289
Epoch 37/1000, Train Loss: 0.6700, Train Accuracy: 0.6492, Val Loss: 0.6880, Val Accuracy: 0.6508
Epoch 38/1000, Train Loss: 0.6695, Train Accuracy: 0.6483, Val Loss: 0.6873, Val Accuracy: 0.6761
Epoch 39/1000, Train Loss: 0.6675, Train Accuracy: 0.6528, Val Loss: 0.6866, Val Accuracy: 0.6960
Epoch 40/1000, Train Loss: 0.6677, Train Accuracy: 0.6483, Val Loss: 0.6859, Val Accuracy: 0.7087
Epoch 41/1000, Train Loss: 0.6656, Train Accuracy: 0.6626, Val Loss: 0.6851, Val Accuracy: 0.7247
Epoch 42/1000, Train Loss: 0.6647, Train Accuracy: 0.6652, Val Loss: 0.6844, Val Accuracy: 0.7339
Epoch 43/1000, Train Loss: 0.6654, Train Accuracy: 0.6575, Val Loss: 0.6836, Val Accuracy: 0.7466
Epoch 44/1000, Train Loss: 0.6637, Train Accuracy: 0.6678, Val Loss: 0.6827, Val Accuracy: 0.7617
Epoch 45/1000, Train Loss: 0.6638, Train Accuracy: 0.6584, Val Loss: 0.6819, Val Accuracy: 0.7680
Epoch 46/1000, Train Loss: 0.6610, Train Accuracy: 0.6752, Val Loss: 0.6810, Val Accuracy: 0.7753
Epoch 47/1000, Train Loss: 0.6602, Train Accuracy: 0.6730, Val Loss: 0.6800, Val Accuracy: 0.7826
Epoch 48/1000, Train Loss: 0.6604, Train Accuracy: 0.6718, Val Loss: 0.6791, Val Accuracy: 0.7875
Epoch 49/1000, Train Loss: 0.6577, Train Accuracy: 0.6819, Val Loss: 0.6780, Val Accuracy: 0.7928
Epoch 50/1000, Train Loss: 0.6577, Train Accuracy: 0.6763, Val Loss: 0.6770, Val Accuracy: 0.7986
Epoch 51/1000, Train Loss: 0.6558, Train Accuracy: 0.6926, Val Loss: 0.6759, Val Accuracy: 0.7972
Epoch 52/1000, Train Loss: 0.6548, Train Accuracy: 0.6847, Val Loss: 0.6749, Val Accuracy: 0.8030
Epoch 53/1000, Train Loss: 0.6552, Train Accuracy: 0.6834, Val Loss: 0.6737, Val Accuracy: 0.8054
Epoch 54/1000, Train Loss: 0.6535, Train Accuracy: 0.6901, Val Loss: 0.6726, Val Accuracy: 0.8074
Epoch 55/1000, Train Loss: 0.6518, Train Accuracy: 0.6921, Val Loss: 0.6715, Val Accuracy: 0.8054
Epoch 56/1000, Train Loss: 0.6523, Train Accuracy: 0.6886, Val Loss: 0.6703, Val Accuracy: 0.8069
Epoch 57/1000, Train Loss: 0.6500, Train Accuracy: 0.6904, Val Loss: 0.6691, Val Accuracy: 0.8089
Epoch 58/1000, Train Loss: 0.6491, Train Accuracy: 0.6870, Val Loss: 0.6679, Val Accuracy: 0.8108
Epoch 59/1000, Train Loss: 0.6483, Train Accuracy: 0.6933, Val Loss: 0.6666, Val Accuracy: 0.8113
Epoch 60/1000, Train Loss: 0.6478, Train Accuracy: 0.6887, Val Loss: 0.6654, Val Accuracy: 0.8142
Epoch 61/1000, Train Loss: 0.6456, Train Accuracy: 0.6983, Val Loss: 0.6642, Val Accuracy: 0.8142
Epoch 62/1000, Train Loss: 0.6443, Train Accuracy: 0.6988, Val Loss: 0.6629, Val Accuracy: 0.8181
Epoch 63/1000, Train Loss: 0.6444, Train Accuracy: 0.6988, Val Loss: 0.6617, Val Accuracy: 0.8196
Epoch 64/1000, Train Loss: 0.6428, Train Accuracy: 0.7044, Val Loss: 0.6604, Val Accuracy: 0.8220
Epoch 65/1000, Train Loss: 0.6417, Train Accuracy: 0.6987, Val Loss: 0.6591, Val Accuracy: 0.8234
Epoch 66/1000, Train Loss: 0.6420, Train Accuracy: 0.6981, Val Loss: 0.6579, Val Accuracy: 0.8225
Epoch 67/1000, Train Loss: 0.6396, Train Accuracy: 0.7059, Val Loss: 0.6567, Val Accuracy: 0.8225
Epoch 68/1000, Train Loss: 0.6398, Train Accuracy: 0.7009, Val Loss: 0.6554, Val Accuracy: 0.8230
Epoch 69/1000, Train Loss: 0.6382, Train Accuracy: 0.6983, Val Loss: 0.6541, Val Accuracy: 0.8239
Epoch 70/1000, Train Loss: 0.6374, Train Accuracy: 0.7004, Val Loss: 0.6529, Val Accuracy: 0.8254
Epoch 71/1000, Train Loss: 0.6358, Train Accuracy: 0.7026, Val Loss: 0.6516, Val Accuracy: 0.8259
Epoch 72/1000, Train Loss: 0.6329, Train Accuracy: 0.7023, Val Loss: 0.6504, Val Accuracy: 0.8273
Epoch 73/1000, Train Loss: 0.6341, Train Accuracy: 0.7077, Val Loss: 0.6492, Val Accuracy: 0.8278
Epoch 74/1000, Train Loss: 0.6336, Train Accuracy: 0.6975, Val Loss: 0.6479, Val Accuracy: 0.8278
Epoch 75/1000, Train Loss: 0.6309, Train Accuracy: 0.7043, Val Loss: 0.6467, Val Accuracy: 0.8288
Epoch 76/1000, Train Loss: 0.6302, Train Accuracy: 0.6989, Val Loss: 0.6454, Val Accuracy: 0.8293
Epoch 77/1000, Train Loss: 0.6302, Train Accuracy: 0.6999, Val Loss: 0.6442, Val Accuracy: 0.8303
Epoch 78/1000, Train Loss: 0.6276, Train Accuracy: 0.7071, Val Loss: 0.6430, Val Accuracy: 0.8312
Epoch 79/1000, Train Loss: 0.6283, Train Accuracy: 0.7033, Val Loss: 0.6417, Val Accuracy: 0.8317
Epoch 80/1000, Train Loss: 0.6266, Train Accuracy: 0.7049, Val Loss: 0.6405, Val Accuracy: 0.8317
Epoch 81/1000, Train Loss: 0.6238, Train Accuracy: 0.7096, Val Loss: 0.6392, Val Accuracy: 0.8307
Epoch 82/1000, Train Loss: 0.6238, Train Accuracy: 0.7025, Val Loss: 0.6380, Val Accuracy: 0.8317
Epoch 83/1000, Train Loss: 0.6232, Train Accuracy: 0.7045, Val Loss: 0.6367, Val Accuracy: 0.8317
Epoch 84/1000, Train Loss: 0.6209, Train Accuracy: 0.7093, Val Loss: 0.6355, Val Accuracy: 0.8317
Epoch 85/1000, Train Loss: 0.6203, Train Accuracy: 0.7049, Val Loss: 0.6343, Val Accuracy: 0.8317
Epoch 86/1000, Train Loss: 0.6190, Train Accuracy: 0.7103, Val Loss: 0.6330, Val Accuracy: 0.8341
Epoch 87/1000, Train Loss: 0.6178, Train Accuracy: 0.7087, Val Loss: 0.6318, Val Accuracy: 0.8341
Epoch 88/1000, Train Loss: 0.6171, Train Accuracy: 0.7066, Val Loss: 0.6305, Val Accuracy: 0.8356
Epoch 89/1000, Train Loss: 0.6154, Train Accuracy: 0.7090, Val Loss: 0.6292, Val Accuracy: 0.8346
Epoch 90/1000, Train Loss: 0.6152, Train Accuracy: 0.7106, Val Loss: 0.6279, Val Accuracy: 0.8366
Epoch 91/1000, Train Loss: 0.6124, Train Accuracy: 0.7090, Val Loss: 0.6267, Val Accuracy: 0.8385
Epoch 92/1000, Train Loss: 0.6123, Train Accuracy: 0.7133, Val Loss: 0.6253, Val Accuracy: 0.8380
Epoch 93/1000, Train Loss: 0.6097, Train Accuracy: 0.7150, Val Loss: 0.6241, Val Accuracy: 0.8380
Epoch 94/1000, Train Loss: 0.6091, Train Accuracy: 0.7126, Val Loss: 0.6227, Val Accuracy: 0.8385
Epoch 95/1000, Train Loss: 0.6078, Train Accuracy: 0.7099, Val Loss: 0.6214, Val Accuracy: 0.8400
Epoch 96/1000, Train Loss: 0.6064, Train Accuracy: 0.7143, Val Loss: 0.6200, Val Accuracy: 0.8405
Epoch 97/1000, Train Loss: 0.6048, Train Accuracy: 0.7179, Val Loss: 0.6187, Val Accuracy: 0.8405
Epoch 98/1000, Train Loss: 0.6034, Train Accuracy: 0.7180, Val Loss: 0.6174, Val Accuracy: 0.8400
Epoch 99/1000, Train Loss: 0.6037, Train Accuracy: 0.7120, Val Loss: 0.6160, Val Accuracy: 0.8405
Epoch 100/1000, Train Loss: 0.6018, Train Accuracy: 0.7176, Val Loss: 0.6146, Val Accuracy: 0.8410
Epoch 101/1000, Train Loss: 0.6002, Train Accuracy: 0.7184, Val Loss: 0.6132, Val Accuracy: 0.8410
Epoch 102/1000, Train Loss: 0.5979, Train Accuracy: 0.7162, Val Loss: 0.6118, Val Accuracy: 0.8405
Epoch 103/1000, Train Loss: 0.5976, Train Accuracy: 0.7184, Val Loss: 0.6104, Val Accuracy: 0.8410
Epoch 104/1000, Train Loss: 0.5958, Train Accuracy: 0.7177, Val Loss: 0.6090, Val Accuracy: 0.8410
Epoch 105/1000, Train Loss: 0.5944, Train Accuracy: 0.7202, Val Loss: 0.6075, Val Accuracy: 0.8434
Epoch 106/1000, Train Loss: 0.5943, Train Accuracy: 0.7249, Val Loss: 0.6061, Val Accuracy: 0.8439
Epoch 107/1000, Train Loss: 0.5918, Train Accuracy: 0.7241, Val Loss: 0.6046, Val Accuracy: 0.8439
Epoch 108/1000, Train Loss: 0.5909, Train Accuracy: 0.7241, Val Loss: 0.6031, Val Accuracy: 0.8458
Epoch 109/1000, Train Loss: 0.5899, Train Accuracy: 0.7257, Val Loss: 0.6017, Val Accuracy: 0.8463
Epoch 110/1000, Train Loss: 0.5875, Train Accuracy: 0.7270, Val Loss: 0.6001, Val Accuracy: 0.8468
Epoch 111/1000, Train Loss: 0.5855, Train Accuracy: 0.7335, Val Loss: 0.5987, Val Accuracy: 0.8478
Epoch 112/1000, Train Loss: 0.5860, Train Accuracy: 0.7231, Val Loss: 0.5972, Val Accuracy: 0.8482
Epoch 113/1000, Train Loss: 0.5815, Train Accuracy: 0.7321, Val Loss: 0.5957, Val Accuracy: 0.8492
Epoch 114/1000, Train Loss: 0.5804, Train Accuracy: 0.7337, Val Loss: 0.5940, Val Accuracy: 0.8502
Epoch 115/1000, Train Loss: 0.5796, Train Accuracy: 0.7394, Val Loss: 0.5922, Val Accuracy: 0.8497
Epoch 116/1000, Train Loss: 0.5795, Train Accuracy: 0.7340, Val Loss: 0.5906, Val Accuracy: 0.8502
Epoch 117/1000, Train Loss: 0.5756, Train Accuracy: 0.7394, Val Loss: 0.5890, Val Accuracy: 0.8507
Epoch 118/1000, Train Loss: 0.5762, Train Accuracy: 0.7397, Val Loss: 0.5873, Val Accuracy: 0.8517
Epoch 119/1000, Train Loss: 0.5737, Train Accuracy: 0.7401, Val Loss: 0.5857, Val Accuracy: 0.8512
Epoch 120/1000, Train Loss: 0.5719, Train Accuracy: 0.7418, Val Loss: 0.5839, Val Accuracy: 0.8521
Epoch 121/1000, Train Loss: 0.5709, Train Accuracy: 0.7454, Val Loss: 0.5822, Val Accuracy: 0.8517
Epoch 122/1000, Train Loss: 0.5682, Train Accuracy: 0.7435, Val Loss: 0.5806, Val Accuracy: 0.8517
Epoch 123/1000, Train Loss: 0.5674, Train Accuracy: 0.7452, Val Loss: 0.5791, Val Accuracy: 0.8512
Epoch 124/1000, Train Loss: 0.5649, Train Accuracy: 0.7495, Val Loss: 0.5774, Val Accuracy: 0.8521
Epoch 125/1000, Train Loss: 0.5640, Train Accuracy: 0.7474, Val Loss: 0.5757, Val Accuracy: 0.8546
Epoch 126/1000, Train Loss: 0.5617, Train Accuracy: 0.7502, Val Loss: 0.5740, Val Accuracy: 0.8551
Epoch 127/1000, Train Loss: 0.5600, Train Accuracy: 0.7547, Val Loss: 0.5722, Val Accuracy: 0.8560
Epoch 128/1000, Train Loss: 0.5596, Train Accuracy: 0.7525, Val Loss: 0.5703, Val Accuracy: 0.8565
Epoch 129/1000, Train Loss: 0.5565, Train Accuracy: 0.7589, Val Loss: 0.5684, Val Accuracy: 0.8570
Epoch 130/1000, Train Loss: 0.5547, Train Accuracy: 0.7584, Val Loss: 0.5667, Val Accuracy: 0.8580
Epoch 131/1000, Train Loss: 0.5538, Train Accuracy: 0.7635, Val Loss: 0.5648, Val Accuracy: 0.8594
Epoch 132/1000, Train Loss: 0.5507, Train Accuracy: 0.7610, Val Loss: 0.5631, Val Accuracy: 0.8604
Epoch 133/1000, Train Loss: 0.5504, Train Accuracy: 0.7643, Val Loss: 0.5612, Val Accuracy: 0.8604
Epoch 134/1000, Train Loss: 0.5473, Train Accuracy: 0.7685, Val Loss: 0.5594, Val Accuracy: 0.8624
Epoch 135/1000, Train Loss: 0.5452, Train Accuracy: 0.7689, Val Loss: 0.5575, Val Accuracy: 0.8638
Epoch 136/1000, Train Loss: 0.5448, Train Accuracy: 0.7705, Val Loss: 0.5556, Val Accuracy: 0.8648
Epoch 137/1000, Train Loss: 0.5418, Train Accuracy: 0.7751, Val Loss: 0.5539, Val Accuracy: 0.8658
Epoch 138/1000, Train Loss: 0.5414, Train Accuracy: 0.7741, Val Loss: 0.5519, Val Accuracy: 0.8658
Epoch 139/1000, Train Loss: 0.5397, Train Accuracy: 0.7731, Val Loss: 0.5499, Val Accuracy: 0.8662
Epoch 140/1000, Train Loss: 0.5374, Train Accuracy: 0.7752, Val Loss: 0.5480, Val Accuracy: 0.8667
Epoch 141/1000, Train Loss: 0.5354, Train Accuracy: 0.7796, Val Loss: 0.5460, Val Accuracy: 0.8672
Epoch 142/1000, Train Loss: 0.5338, Train Accuracy: 0.7846, Val Loss: 0.5440, Val Accuracy: 0.8677
Epoch 143/1000, Train Loss: 0.5304, Train Accuracy: 0.7845, Val Loss: 0.5420, Val Accuracy: 0.8672
Epoch 144/1000, Train Loss: 0.5298, Train Accuracy: 0.7851, Val Loss: 0.5401, Val Accuracy: 0.8677
Epoch 145/1000, Train Loss: 0.5270, Train Accuracy: 0.7862, Val Loss: 0.5381, Val Accuracy: 0.8696
Epoch 146/1000, Train Loss: 0.5246, Train Accuracy: 0.7865, Val Loss: 0.5360, Val Accuracy: 0.8701
Epoch 147/1000, Train Loss: 0.5232, Train Accuracy: 0.7954, Val Loss: 0.5339, Val Accuracy: 0.8692
Epoch 148/1000, Train Loss: 0.5210, Train Accuracy: 0.7954, Val Loss: 0.5319, Val Accuracy: 0.8696
Epoch 149/1000, Train Loss: 0.5197, Train Accuracy: 0.7919, Val Loss: 0.5300, Val Accuracy: 0.8701
Epoch 150/1000, Train Loss: 0.5179, Train Accuracy: 0.8009, Val Loss: 0.5280, Val Accuracy: 0.8721
Epoch 151/1000, Train Loss: 0.5161, Train Accuracy: 0.7983, Val Loss: 0.5259, Val Accuracy: 0.8735
Epoch 152/1000, Train Loss: 0.5122, Train Accuracy: 0.8015, Val Loss: 0.5238, Val Accuracy: 0.8735
Epoch 153/1000, Train Loss: 0.5120, Train Accuracy: 0.8036, Val Loss: 0.5217, Val Accuracy: 0.8750
Epoch 154/1000, Train Loss: 0.5108, Train Accuracy: 0.8073, Val Loss: 0.5195, Val Accuracy: 0.8755
Epoch 155/1000, Train Loss: 0.5083, Train Accuracy: 0.8057, Val Loss: 0.5175, Val Accuracy: 0.8755
Epoch 156/1000, Train Loss: 0.5068, Train Accuracy: 0.8023, Val Loss: 0.5155, Val Accuracy: 0.8755
Epoch 157/1000, Train Loss: 0.5019, Train Accuracy: 0.8138, Val Loss: 0.5134, Val Accuracy: 0.8765
Epoch 158/1000, Train Loss: 0.5029, Train Accuracy: 0.8121, Val Loss: 0.5114, Val Accuracy: 0.8784
Epoch 159/1000, Train Loss: 0.5011, Train Accuracy: 0.8116, Val Loss: 0.5093, Val Accuracy: 0.8784
Epoch 160/1000, Train Loss: 0.4989, Train Accuracy: 0.8132, Val Loss: 0.5072, Val Accuracy: 0.8794
Epoch 161/1000, Train Loss: 0.4947, Train Accuracy: 0.8203, Val Loss: 0.5051, Val Accuracy: 0.8799
Epoch 162/1000, Train Loss: 0.4945, Train Accuracy: 0.8140, Val Loss: 0.5030, Val Accuracy: 0.8799
Epoch 163/1000, Train Loss: 0.4924, Train Accuracy: 0.8207, Val Loss: 0.5008, Val Accuracy: 0.8804
Epoch 164/1000, Train Loss: 0.4896, Train Accuracy: 0.8173, Val Loss: 0.4989, Val Accuracy: 0.8804
Epoch 165/1000, Train Loss: 0.4874, Train Accuracy: 0.8280, Val Loss: 0.4966, Val Accuracy: 0.8799
Epoch 166/1000, Train Loss: 0.4834, Train Accuracy: 0.8295, Val Loss: 0.4945, Val Accuracy: 0.8799
Epoch 167/1000, Train Loss: 0.4835, Train Accuracy: 0.8286, Val Loss: 0.4924, Val Accuracy: 0.8808
Epoch 168/1000, Train Loss: 0.4800, Train Accuracy: 0.8308, Val Loss: 0.4903, Val Accuracy: 0.8808
Epoch 169/1000, Train Loss: 0.4783, Train Accuracy: 0.8319, Val Loss: 0.4880, Val Accuracy: 0.8813
Epoch 170/1000, Train Loss: 0.4793, Train Accuracy: 0.8285, Val Loss: 0.4859, Val Accuracy: 0.8823
Epoch 171/1000, Train Loss: 0.4732, Train Accuracy: 0.8343, Val Loss: 0.4837, Val Accuracy: 0.8823
Epoch 172/1000, Train Loss: 0.4740, Train Accuracy: 0.8343, Val Loss: 0.4815, Val Accuracy: 0.8828
Epoch 173/1000, Train Loss: 0.4694, Train Accuracy: 0.8394, Val Loss: 0.4793, Val Accuracy: 0.8847
Epoch 174/1000, Train Loss: 0.4679, Train Accuracy: 0.8417, Val Loss: 0.4771, Val Accuracy: 0.8852
Epoch 175/1000, Train Loss: 0.4673, Train Accuracy: 0.8365, Val Loss: 0.4751, Val Accuracy: 0.8852
Epoch 176/1000, Train Loss: 0.4669, Train Accuracy: 0.8414, Val Loss: 0.4727, Val Accuracy: 0.8872
Epoch 177/1000, Train Loss: 0.4616, Train Accuracy: 0.8490, Val Loss: 0.4706, Val Accuracy: 0.8867
Epoch 178/1000, Train Loss: 0.4617, Train Accuracy: 0.8420, Val Loss: 0.4686, Val Accuracy: 0.8876
Epoch 179/1000, Train Loss: 0.4584, Train Accuracy: 0.8489, Val Loss: 0.4664, Val Accuracy: 0.8872
Epoch 180/1000, Train Loss: 0.4565, Train Accuracy: 0.8477, Val Loss: 0.4642, Val Accuracy: 0.8876
Epoch 181/1000, Train Loss: 0.4523, Train Accuracy: 0.8494, Val Loss: 0.4620, Val Accuracy: 0.8876
Epoch 182/1000, Train Loss: 0.4510, Train Accuracy: 0.8542, Val Loss: 0.4599, Val Accuracy: 0.8881
Epoch 183/1000, Train Loss: 0.4503, Train Accuracy: 0.8512, Val Loss: 0.4578, Val Accuracy: 0.8881
Epoch 184/1000, Train Loss: 0.4490, Train Accuracy: 0.8551, Val Loss: 0.4556, Val Accuracy: 0.8886
Epoch 185/1000, Train Loss: 0.4460, Train Accuracy: 0.8511, Val Loss: 0.4536, Val Accuracy: 0.8886
Epoch 186/1000, Train Loss: 0.4439, Train Accuracy: 0.8589, Val Loss: 0.4515, Val Accuracy: 0.8901
Epoch 187/1000, Train Loss: 0.4409, Train Accuracy: 0.8616, Val Loss: 0.4493, Val Accuracy: 0.8906
Epoch 188/1000, Train Loss: 0.4384, Train Accuracy: 0.8594, Val Loss: 0.4473, Val Accuracy: 0.8901
Epoch 189/1000, Train Loss: 0.4369, Train Accuracy: 0.8587, Val Loss: 0.4451, Val Accuracy: 0.8901
Epoch 190/1000, Train Loss: 0.4381, Train Accuracy: 0.8557, Val Loss: 0.4431, Val Accuracy: 0.8891
Epoch 191/1000, Train Loss: 0.4345, Train Accuracy: 0.8627, Val Loss: 0.4409, Val Accuracy: 0.8881
Epoch 192/1000, Train Loss: 0.4323, Train Accuracy: 0.8649, Val Loss: 0.4387, Val Accuracy: 0.8891
Epoch 193/1000, Train Loss: 0.4282, Train Accuracy: 0.8683, Val Loss: 0.4367, Val Accuracy: 0.8876
Epoch 194/1000, Train Loss: 0.4265, Train Accuracy: 0.8643, Val Loss: 0.4345, Val Accuracy: 0.8876
Epoch 195/1000, Train Loss: 0.4244, Train Accuracy: 0.8669, Val Loss: 0.4325, Val Accuracy: 0.8876
Epoch 196/1000, Train Loss: 0.4240, Train Accuracy: 0.8646, Val Loss: 0.4306, Val Accuracy: 0.8872
Epoch 197/1000, Train Loss: 0.4215, Train Accuracy: 0.8700, Val Loss: 0.4284, Val Accuracy: 0.8876
Epoch 198/1000, Train Loss: 0.4189, Train Accuracy: 0.8673, Val Loss: 0.4263, Val Accuracy: 0.8886
Epoch 199/1000, Train Loss: 0.4182, Train Accuracy: 0.8712, Val Loss: 0.4240, Val Accuracy: 0.8891
Epoch 200/1000, Train Loss: 0.4136, Train Accuracy: 0.8737, Val Loss: 0.4219, Val Accuracy: 0.8896
Epoch 201/1000, Train Loss: 0.4111, Train Accuracy: 0.8742, Val Loss: 0.4199, Val Accuracy: 0.8901
Epoch 202/1000, Train Loss: 0.4101, Train Accuracy: 0.8747, Val Loss: 0.4178, Val Accuracy: 0.8915
Epoch 203/1000, Train Loss: 0.4076, Train Accuracy: 0.8735, Val Loss: 0.4157, Val Accuracy: 0.8915
Epoch 204/1000, Train Loss: 0.4059, Train Accuracy: 0.8790, Val Loss: 0.4137, Val Accuracy: 0.8915
Epoch 205/1000, Train Loss: 0.4050, Train Accuracy: 0.8737, Val Loss: 0.4116, Val Accuracy: 0.8915
Epoch 206/1000, Train Loss: 0.4025, Train Accuracy: 0.8768, Val Loss: 0.4096, Val Accuracy: 0.8930
Epoch 207/1000, Train Loss: 0.4001, Train Accuracy: 0.8797, Val Loss: 0.4074, Val Accuracy: 0.8935
Epoch 208/1000, Train Loss: 0.3987, Train Accuracy: 0.8743, Val Loss: 0.4054, Val Accuracy: 0.8945
Epoch 209/1000, Train Loss: 0.3967, Train Accuracy: 0.8743, Val Loss: 0.4033, Val Accuracy: 0.8945
Epoch 210/1000, Train Loss: 0.3943, Train Accuracy: 0.8818, Val Loss: 0.4014, Val Accuracy: 0.8935
Epoch 211/1000, Train Loss: 0.3920, Train Accuracy: 0.8833, Val Loss: 0.3993, Val Accuracy: 0.8940
Epoch 212/1000, Train Loss: 0.3890, Train Accuracy: 0.8824, Val Loss: 0.3974, Val Accuracy: 0.8935
Epoch 213/1000, Train Loss: 0.3884, Train Accuracy: 0.8831, Val Loss: 0.3953, Val Accuracy: 0.8945
Epoch 214/1000, Train Loss: 0.3841, Train Accuracy: 0.8869, Val Loss: 0.3932, Val Accuracy: 0.8940
Epoch 215/1000, Train Loss: 0.3856, Train Accuracy: 0.8844, Val Loss: 0.3910, Val Accuracy: 0.8940
Epoch 216/1000, Train Loss: 0.3823, Train Accuracy: 0.8836, Val Loss: 0.3889, Val Accuracy: 0.8945
Epoch 217/1000, Train Loss: 0.3813, Train Accuracy: 0.8820, Val Loss: 0.3868, Val Accuracy: 0.8949
Epoch 218/1000, Train Loss: 0.3794, Train Accuracy: 0.8905, Val Loss: 0.3847, Val Accuracy: 0.8945
Epoch 219/1000, Train Loss: 0.3764, Train Accuracy: 0.8872, Val Loss: 0.3827, Val Accuracy: 0.8949
Epoch 220/1000, Train Loss: 0.3739, Train Accuracy: 0.8904, Val Loss: 0.3806, Val Accuracy: 0.8949
Epoch 221/1000, Train Loss: 0.3722, Train Accuracy: 0.8872, Val Loss: 0.3786, Val Accuracy: 0.8949
Epoch 222/1000, Train Loss: 0.3693, Train Accuracy: 0.8874, Val Loss: 0.3764, Val Accuracy: 0.8954
Epoch 223/1000, Train Loss: 0.3667, Train Accuracy: 0.8894, Val Loss: 0.3746, Val Accuracy: 0.8954
Epoch 224/1000, Train Loss: 0.3665, Train Accuracy: 0.8898, Val Loss: 0.3728, Val Accuracy: 0.8954
Epoch 225/1000, Train Loss: 0.3641, Train Accuracy: 0.8916, Val Loss: 0.3710, Val Accuracy: 0.8949
Epoch 226/1000, Train Loss: 0.3637, Train Accuracy: 0.8889, Val Loss: 0.3691, Val Accuracy: 0.8954
Epoch 227/1000, Train Loss: 0.3610, Train Accuracy: 0.8906, Val Loss: 0.3672, Val Accuracy: 0.8954
Epoch 228/1000, Train Loss: 0.3599, Train Accuracy: 0.8917, Val Loss: 0.3654, Val Accuracy: 0.8959
Epoch 229/1000, Train Loss: 0.3583, Train Accuracy: 0.8943, Val Loss: 0.3634, Val Accuracy: 0.8959
Epoch 230/1000, Train Loss: 0.3551, Train Accuracy: 0.8962, Val Loss: 0.3616, Val Accuracy: 0.8954
Epoch 231/1000, Train Loss: 0.3534, Train Accuracy: 0.8921, Val Loss: 0.3598, Val Accuracy: 0.8964
Epoch 232/1000, Train Loss: 0.3524, Train Accuracy: 0.8955, Val Loss: 0.3579, Val Accuracy: 0.8974
Epoch 233/1000, Train Loss: 0.3505, Train Accuracy: 0.8950, Val Loss: 0.3561, Val Accuracy: 0.8974
Epoch 234/1000, Train Loss: 0.3498, Train Accuracy: 0.8939, Val Loss: 0.3542, Val Accuracy: 0.8974
Epoch 235/1000, Train Loss: 0.3474, Train Accuracy: 0.8934, Val Loss: 0.3523, Val Accuracy: 0.8969
Epoch 236/1000, Train Loss: 0.3463, Train Accuracy: 0.8948, Val Loss: 0.3506, Val Accuracy: 0.8974
Epoch 237/1000, Train Loss: 0.3420, Train Accuracy: 0.8956, Val Loss: 0.3488, Val Accuracy: 0.8974
Epoch 238/1000, Train Loss: 0.3409, Train Accuracy: 0.8975, Val Loss: 0.3470, Val Accuracy: 0.8974
Epoch 239/1000, Train Loss: 0.3406, Train Accuracy: 0.8958, Val Loss: 0.3451, Val Accuracy: 0.8979
Epoch 240/1000, Train Loss: 0.3377, Train Accuracy: 0.8990, Val Loss: 0.3433, Val Accuracy: 0.8979
Epoch 241/1000, Train Loss: 0.3367, Train Accuracy: 0.8972, Val Loss: 0.3415, Val Accuracy: 0.8983
Epoch 242/1000, Train Loss: 0.3369, Train Accuracy: 0.8972, Val Loss: 0.3398, Val Accuracy: 0.8979
Epoch 243/1000, Train Loss: 0.3333, Train Accuracy: 0.8988, Val Loss: 0.3380, Val Accuracy: 0.8979
Epoch 244/1000, Train Loss: 0.3313, Train Accuracy: 0.8973, Val Loss: 0.3363, Val Accuracy: 0.8974
Epoch 245/1000, Train Loss: 0.3300, Train Accuracy: 0.8971, Val Loss: 0.3347, Val Accuracy: 0.8979
Epoch 246/1000, Train Loss: 0.3291, Train Accuracy: 0.8978, Val Loss: 0.3330, Val Accuracy: 0.8983
Epoch 247/1000, Train Loss: 0.3249, Train Accuracy: 0.9034, Val Loss: 0.3315, Val Accuracy: 0.8983
Epoch 248/1000, Train Loss: 0.3237, Train Accuracy: 0.8990, Val Loss: 0.3299, Val Accuracy: 0.8983
Epoch 249/1000, Train Loss: 0.3206, Train Accuracy: 0.9055, Val Loss: 0.3280, Val Accuracy: 0.8993
Epoch 250/1000, Train Loss: 0.3233, Train Accuracy: 0.9020, Val Loss: 0.3264, Val Accuracy: 0.8993
Epoch 251/1000, Train Loss: 0.3192, Train Accuracy: 0.9040, Val Loss: 0.3247, Val Accuracy: 0.9008
Epoch 252/1000, Train Loss: 0.3186, Train Accuracy: 0.9024, Val Loss: 0.3229, Val Accuracy: 0.9018
Epoch 253/1000, Train Loss: 0.3169, Train Accuracy: 0.9033, Val Loss: 0.3215, Val Accuracy: 0.9008
Epoch 254/1000, Train Loss: 0.3155, Train Accuracy: 0.9057, Val Loss: 0.3199, Val Accuracy: 0.9013
Epoch 255/1000, Train Loss: 0.3127, Train Accuracy: 0.9074, Val Loss: 0.3183, Val Accuracy: 0.9013
Epoch 256/1000, Train Loss: 0.3118, Train Accuracy: 0.9045, Val Loss: 0.3168, Val Accuracy: 0.9013
Epoch 257/1000, Train Loss: 0.3105, Train Accuracy: 0.9041, Val Loss: 0.3151, Val Accuracy: 0.9018
Epoch 258/1000, Train Loss: 0.3109, Train Accuracy: 0.9040, Val Loss: 0.3136, Val Accuracy: 0.9013
Epoch 259/1000, Train Loss: 0.3033, Train Accuracy: 0.9082, Val Loss: 0.3122, Val Accuracy: 0.9013
Epoch 260/1000, Train Loss: 0.3078, Train Accuracy: 0.9050, Val Loss: 0.3107, Val Accuracy: 0.9013
Epoch 261/1000, Train Loss: 0.3042, Train Accuracy: 0.9056, Val Loss: 0.3092, Val Accuracy: 0.9018
Epoch 262/1000, Train Loss: 0.3017, Train Accuracy: 0.9085, Val Loss: 0.3078, Val Accuracy: 0.9008
Epoch 263/1000, Train Loss: 0.3024, Train Accuracy: 0.9063, Val Loss: 0.3062, Val Accuracy: 0.9013
Epoch 264/1000, Train Loss: 0.2986, Train Accuracy: 0.9083, Val Loss: 0.3047, Val Accuracy: 0.9013
Epoch 265/1000, Train Loss: 0.2989, Train Accuracy: 0.9056, Val Loss: 0.3032, Val Accuracy: 0.9018
Epoch 266/1000, Train Loss: 0.2971, Train Accuracy: 0.9062, Val Loss: 0.3019, Val Accuracy: 0.9008
Epoch 267/1000, Train Loss: 0.2931, Train Accuracy: 0.9091, Val Loss: 0.3006, Val Accuracy: 0.9013
Epoch 268/1000, Train Loss: 0.2947, Train Accuracy: 0.9101, Val Loss: 0.2993, Val Accuracy: 0.9013
Epoch 269/1000, Train Loss: 0.2908, Train Accuracy: 0.9108, Val Loss: 0.2977, Val Accuracy: 0.9013
Epoch 270/1000, Train Loss: 0.2930, Train Accuracy: 0.9069, Val Loss: 0.2962, Val Accuracy: 0.9022
Epoch 271/1000, Train Loss: 0.2881, Train Accuracy: 0.9093, Val Loss: 0.2949, Val Accuracy: 0.9022
Epoch 272/1000, Train Loss: 0.2895, Train Accuracy: 0.9088, Val Loss: 0.2935, Val Accuracy: 0.9022
Epoch 273/1000, Train Loss: 0.2859, Train Accuracy: 0.9105, Val Loss: 0.2921, Val Accuracy: 0.9032
Epoch 274/1000, Train Loss: 0.2851, Train Accuracy: 0.9139, Val Loss: 0.2908, Val Accuracy: 0.9032
Epoch 275/1000, Train Loss: 0.2837, Train Accuracy: 0.9114, Val Loss: 0.2897, Val Accuracy: 0.9032
Epoch 276/1000, Train Loss: 0.2813, Train Accuracy: 0.9118, Val Loss: 0.2883, Val Accuracy: 0.9032
Epoch 277/1000, Train Loss: 0.2817, Train Accuracy: 0.9102, Val Loss: 0.2870, Val Accuracy: 0.9042
Epoch 278/1000, Train Loss: 0.2778, Train Accuracy: 0.9135, Val Loss: 0.2855, Val Accuracy: 0.9042
Epoch 279/1000, Train Loss: 0.2764, Train Accuracy: 0.9138, Val Loss: 0.2840, Val Accuracy: 0.9047
Epoch 280/1000, Train Loss: 0.2787, Train Accuracy: 0.9106, Val Loss: 0.2829, Val Accuracy: 0.9047
Epoch 281/1000, Train Loss: 0.2741, Train Accuracy: 0.9100, Val Loss: 0.2815, Val Accuracy: 0.9052
Epoch 282/1000, Train Loss: 0.2742, Train Accuracy: 0.9141, Val Loss: 0.2803, Val Accuracy: 0.9052
Epoch 283/1000, Train Loss: 0.2750, Train Accuracy: 0.9123, Val Loss: 0.2791, Val Accuracy: 0.9056
Epoch 284/1000, Train Loss: 0.2734, Train Accuracy: 0.9150, Val Loss: 0.2779, Val Accuracy: 0.9056
Epoch 285/1000, Train Loss: 0.2718, Train Accuracy: 0.9141, Val Loss: 0.2766, Val Accuracy: 0.9061
Epoch 286/1000, Train Loss: 0.2705, Train Accuracy: 0.9139, Val Loss: 0.2756, Val Accuracy: 0.9056
Epoch 287/1000, Train Loss: 0.2699, Train Accuracy: 0.9140, Val Loss: 0.2743, Val Accuracy: 0.9056
Epoch 288/1000, Train Loss: 0.2669, Train Accuracy: 0.9172, Val Loss: 0.2732, Val Accuracy: 0.9056
Epoch 289/1000, Train Loss: 0.2665, Train Accuracy: 0.9150, Val Loss: 0.2720, Val Accuracy: 0.9061
Epoch 290/1000, Train Loss: 0.2664, Train Accuracy: 0.9162, Val Loss: 0.2709, Val Accuracy: 0.9066
Epoch 291/1000, Train Loss: 0.2663, Train Accuracy: 0.9149, Val Loss: 0.2698, Val Accuracy: 0.9071
Epoch 292/1000, Train Loss: 0.2628, Train Accuracy: 0.9197, Val Loss: 0.2686, Val Accuracy: 0.9066
Epoch 293/1000, Train Loss: 0.2630, Train Accuracy: 0.9147, Val Loss: 0.2674, Val Accuracy: 0.9071
Epoch 294/1000, Train Loss: 0.2606, Train Accuracy: 0.9149, Val Loss: 0.2663, Val Accuracy: 0.9076
Epoch 295/1000, Train Loss: 0.2591, Train Accuracy: 0.9195, Val Loss: 0.2651, Val Accuracy: 0.9076
Epoch 296/1000, Train Loss: 0.2597, Train Accuracy: 0.9136, Val Loss: 0.2639, Val Accuracy: 0.9076
Epoch 297/1000, Train Loss: 0.2577, Train Accuracy: 0.9180, Val Loss: 0.2627, Val Accuracy: 0.9081
Epoch 298/1000, Train Loss: 0.2582, Train Accuracy: 0.9152, Val Loss: 0.2615, Val Accuracy: 0.9081
Epoch 299/1000, Train Loss: 0.2576, Train Accuracy: 0.9145, Val Loss: 0.2604, Val Accuracy: 0.9081
Epoch 300/1000, Train Loss: 0.2532, Train Accuracy: 0.9170, Val Loss: 0.2594, Val Accuracy: 0.9086
Epoch 301/1000, Train Loss: 0.2501, Train Accuracy: 0.9187, Val Loss: 0.2583, Val Accuracy: 0.9086
Epoch 302/1000, Train Loss: 0.2513, Train Accuracy: 0.9173, Val Loss: 0.2575, Val Accuracy: 0.9086
Epoch 303/1000, Train Loss: 0.2495, Train Accuracy: 0.9225, Val Loss: 0.2565, Val Accuracy: 0.9086
Epoch 304/1000, Train Loss: 0.2502, Train Accuracy: 0.9162, Val Loss: 0.2555, Val Accuracy: 0.9090
Epoch 305/1000, Train Loss: 0.2491, Train Accuracy: 0.9200, Val Loss: 0.2547, Val Accuracy: 0.9105
Epoch 306/1000, Train Loss: 0.2493, Train Accuracy: 0.9200, Val Loss: 0.2539, Val Accuracy: 0.9100
Epoch 307/1000, Train Loss: 0.2479, Train Accuracy: 0.9187, Val Loss: 0.2529, Val Accuracy: 0.9100
Epoch 308/1000, Train Loss: 0.2453, Train Accuracy: 0.9212, Val Loss: 0.2519, Val Accuracy: 0.9100
Epoch 309/1000, Train Loss: 0.2422, Train Accuracy: 0.9212, Val Loss: 0.2510, Val Accuracy: 0.9100
Epoch 310/1000, Train Loss: 0.2446, Train Accuracy: 0.9214, Val Loss: 0.2499, Val Accuracy: 0.9105
Epoch 311/1000, Train Loss: 0.2438, Train Accuracy: 0.9207, Val Loss: 0.2489, Val Accuracy: 0.9110
Epoch 312/1000, Train Loss: 0.2450, Train Accuracy: 0.9178, Val Loss: 0.2479, Val Accuracy: 0.9110
Epoch 313/1000, Train Loss: 0.2378, Train Accuracy: 0.9230, Val Loss: 0.2470, Val Accuracy: 0.9115
Epoch 314/1000, Train Loss: 0.2389, Train Accuracy: 0.9228, Val Loss: 0.2461, Val Accuracy: 0.9115
Epoch 315/1000, Train Loss: 0.2403, Train Accuracy: 0.9189, Val Loss: 0.2452, Val Accuracy: 0.9115
Epoch 316/1000, Train Loss: 0.2372, Train Accuracy: 0.9225, Val Loss: 0.2441, Val Accuracy: 0.9115
Epoch 317/1000, Train Loss: 0.2371, Train Accuracy: 0.9231, Val Loss: 0.2433, Val Accuracy: 0.9115
Epoch 318/1000, Train Loss: 0.2397, Train Accuracy: 0.9200, Val Loss: 0.2424, Val Accuracy: 0.9115
Epoch 319/1000, Train Loss: 0.2358, Train Accuracy: 0.9202, Val Loss: 0.2415, Val Accuracy: 0.9120
Epoch 320/1000, Train Loss: 0.2325, Train Accuracy: 0.9247, Val Loss: 0.2405, Val Accuracy: 0.9129
Epoch 321/1000, Train Loss: 0.2355, Train Accuracy: 0.9232, Val Loss: 0.2400, Val Accuracy: 0.9134
Epoch 322/1000, Train Loss: 0.2321, Train Accuracy: 0.9259, Val Loss: 0.2392, Val Accuracy: 0.9125
Epoch 323/1000, Train Loss: 0.2327, Train Accuracy: 0.9226, Val Loss: 0.2382, Val Accuracy: 0.9134
Epoch 324/1000, Train Loss: 0.2333, Train Accuracy: 0.9263, Val Loss: 0.2373, Val Accuracy: 0.9134
Epoch 325/1000, Train Loss: 0.2280, Train Accuracy: 0.9265, Val Loss: 0.2362, Val Accuracy: 0.9149
Epoch 326/1000, Train Loss: 0.2311, Train Accuracy: 0.9218, Val Loss: 0.2352, Val Accuracy: 0.9159
Epoch 327/1000, Train Loss: 0.2305, Train Accuracy: 0.9218, Val Loss: 0.2346, Val Accuracy: 0.9159
Epoch 328/1000, Train Loss: 0.2278, Train Accuracy: 0.9243, Val Loss: 0.2339, Val Accuracy: 0.9149
Epoch 329/1000, Train Loss: 0.2258, Train Accuracy: 0.9256, Val Loss: 0.2334, Val Accuracy: 0.9144
Epoch 330/1000, Train Loss: 0.2252, Train Accuracy: 0.9269, Val Loss: 0.2327, Val Accuracy: 0.9139
Epoch 331/1000, Train Loss: 0.2237, Train Accuracy: 0.9259, Val Loss: 0.2316, Val Accuracy: 0.9144
Epoch 332/1000, Train Loss: 0.2240, Train Accuracy: 0.9271, Val Loss: 0.2307, Val Accuracy: 0.9144
Epoch 333/1000, Train Loss: 0.2224, Train Accuracy: 0.9246, Val Loss: 0.2300, Val Accuracy: 0.9149
Epoch 334/1000, Train Loss: 0.2212, Train Accuracy: 0.9282, Val Loss: 0.2293, Val Accuracy: 0.9149
Epoch 335/1000, Train Loss: 0.2220, Train Accuracy: 0.9274, Val Loss: 0.2286, Val Accuracy: 0.9154
Epoch 336/1000, Train Loss: 0.2210, Train Accuracy: 0.9260, Val Loss: 0.2278, Val Accuracy: 0.9159
Epoch 337/1000, Train Loss: 0.2207, Train Accuracy: 0.9260, Val Loss: 0.2273, Val Accuracy: 0.9159
Epoch 338/1000, Train Loss: 0.2204, Train Accuracy: 0.9275, Val Loss: 0.2268, Val Accuracy: 0.9154
Epoch 339/1000, Train Loss: 0.2178, Train Accuracy: 0.9274, Val Loss: 0.2261, Val Accuracy: 0.9168
Epoch 340/1000, Train Loss: 0.2191, Train Accuracy: 0.9260, Val Loss: 0.2254, Val Accuracy: 0.9168
Epoch 341/1000, Train Loss: 0.2173, Train Accuracy: 0.9299, Val Loss: 0.2245, Val Accuracy: 0.9178
Epoch 342/1000, Train Loss: 0.2166, Train Accuracy: 0.9242, Val Loss: 0.2238, Val Accuracy: 0.9183
Epoch 343/1000, Train Loss: 0.2160, Train Accuracy: 0.9271, Val Loss: 0.2230, Val Accuracy: 0.9188
Epoch 344/1000, Train Loss: 0.2146, Train Accuracy: 0.9285, Val Loss: 0.2222, Val Accuracy: 0.9188
Epoch 345/1000, Train Loss: 0.2159, Train Accuracy: 0.9240, Val Loss: 0.2216, Val Accuracy: 0.9188
Epoch 346/1000, Train Loss: 0.2110, Train Accuracy: 0.9299, Val Loss: 0.2209, Val Accuracy: 0.9188
Epoch 347/1000, Train Loss: 0.2141, Train Accuracy: 0.9254, Val Loss: 0.2204, Val Accuracy: 0.9188
Epoch 348/1000, Train Loss: 0.2127, Train Accuracy: 0.9277, Val Loss: 0.2199, Val Accuracy: 0.9188
Epoch 349/1000, Train Loss: 0.2061, Train Accuracy: 0.9288, Val Loss: 0.2190, Val Accuracy: 0.9188
Epoch 350/1000, Train Loss: 0.2106, Train Accuracy: 0.9275, Val Loss: 0.2183, Val Accuracy: 0.9193
Epoch 351/1000, Train Loss: 0.2081, Train Accuracy: 0.9296, Val Loss: 0.2176, Val Accuracy: 0.9197
Epoch 352/1000, Train Loss: 0.2090, Train Accuracy: 0.9269, Val Loss: 0.2170, Val Accuracy: 0.9193
Epoch 353/1000, Train Loss: 0.2105, Train Accuracy: 0.9285, Val Loss: 0.2163, Val Accuracy: 0.9193
Epoch 354/1000, Train Loss: 0.2092, Train Accuracy: 0.9274, Val Loss: 0.2159, Val Accuracy: 0.9193
Epoch 355/1000, Train Loss: 0.2056, Train Accuracy: 0.9302, Val Loss: 0.2150, Val Accuracy: 0.9197
Epoch 356/1000, Train Loss: 0.2052, Train Accuracy: 0.9299, Val Loss: 0.2142, Val Accuracy: 0.9212
Epoch 357/1000, Train Loss: 0.2047, Train Accuracy: 0.9312, Val Loss: 0.2137, Val Accuracy: 0.9212
Epoch 358/1000, Train Loss: 0.2010, Train Accuracy: 0.9326, Val Loss: 0.2131, Val Accuracy: 0.9212
Epoch 359/1000, Train Loss: 0.2023, Train Accuracy: 0.9322, Val Loss: 0.2124, Val Accuracy: 0.9217
Epoch 360/1000, Train Loss: 0.2015, Train Accuracy: 0.9302, Val Loss: 0.2117, Val Accuracy: 0.9227
Epoch 361/1000, Train Loss: 0.2058, Train Accuracy: 0.9279, Val Loss: 0.2114, Val Accuracy: 0.9227
Epoch 362/1000, Train Loss: 0.2038, Train Accuracy: 0.9325, Val Loss: 0.2109, Val Accuracy: 0.9232
Epoch 363/1000, Train Loss: 0.1990, Train Accuracy: 0.9326, Val Loss: 0.2104, Val Accuracy: 0.9232
Epoch 364/1000, Train Loss: 0.1990, Train Accuracy: 0.9324, Val Loss: 0.2098, Val Accuracy: 0.9232
Epoch 365/1000, Train Loss: 0.2017, Train Accuracy: 0.9301, Val Loss: 0.2092, Val Accuracy: 0.9241
Epoch 366/1000, Train Loss: 0.1983, Train Accuracy: 0.9329, Val Loss: 0.2085, Val Accuracy: 0.9241
Epoch 367/1000, Train Loss: 0.1976, Train Accuracy: 0.9339, Val Loss: 0.2079, Val Accuracy: 0.9241
Epoch 368/1000, Train Loss: 0.1991, Train Accuracy: 0.9332, Val Loss: 0.2073, Val Accuracy: 0.9241
Epoch 369/1000, Train Loss: 0.1970, Train Accuracy: 0.9293, Val Loss: 0.2068, Val Accuracy: 0.9241
Epoch 370/1000, Train Loss: 0.1973, Train Accuracy: 0.9318, Val Loss: 0.2063, Val Accuracy: 0.9246
Epoch 371/1000, Train Loss: 0.1967, Train Accuracy: 0.9326, Val Loss: 0.2058, Val Accuracy: 0.9246
Epoch 372/1000, Train Loss: 0.1956, Train Accuracy: 0.9352, Val Loss: 0.2052, Val Accuracy: 0.9251
Epoch 373/1000, Train Loss: 0.1961, Train Accuracy: 0.9308, Val Loss: 0.2048, Val Accuracy: 0.9261
Epoch 374/1000, Train Loss: 0.1948, Train Accuracy: 0.9336, Val Loss: 0.2040, Val Accuracy: 0.9261
Epoch 375/1000, Train Loss: 0.1966, Train Accuracy: 0.9330, Val Loss: 0.2034, Val Accuracy: 0.9266
Epoch 376/1000, Train Loss: 0.1930, Train Accuracy: 0.9361, Val Loss: 0.2029, Val Accuracy: 0.9261
Epoch 377/1000, Train Loss: 0.1950, Train Accuracy: 0.9349, Val Loss: 0.2024, Val Accuracy: 0.9261
Epoch 378/1000, Train Loss: 0.1926, Train Accuracy: 0.9325, Val Loss: 0.2019, Val Accuracy: 0.9266
Epoch 379/1000, Train Loss: 0.1919, Train Accuracy: 0.9350, Val Loss: 0.2014, Val Accuracy: 0.9266
Epoch 380/1000, Train Loss: 0.1895, Train Accuracy: 0.9361, Val Loss: 0.2009, Val Accuracy: 0.9266
Epoch 381/1000, Train Loss: 0.1917, Train Accuracy: 0.9348, Val Loss: 0.2005, Val Accuracy: 0.9270
Epoch 382/1000, Train Loss: 0.1905, Train Accuracy: 0.9354, Val Loss: 0.2000, Val Accuracy: 0.9270
Epoch 383/1000, Train Loss: 0.1868, Train Accuracy: 0.9371, Val Loss: 0.1994, Val Accuracy: 0.9275
Epoch 384/1000, Train Loss: 0.1892, Train Accuracy: 0.9367, Val Loss: 0.1988, Val Accuracy: 0.9266
Epoch 385/1000, Train Loss: 0.1890, Train Accuracy: 0.9369, Val Loss: 0.1984, Val Accuracy: 0.9266
Epoch 386/1000, Train Loss: 0.1886, Train Accuracy: 0.9381, Val Loss: 0.1978, Val Accuracy: 0.9266
Epoch 387/1000, Train Loss: 0.1899, Train Accuracy: 0.9343, Val Loss: 0.1974, Val Accuracy: 0.9275
Epoch 388/1000, Train Loss: 0.1868, Train Accuracy: 0.9357, Val Loss: 0.1970, Val Accuracy: 0.9280
Epoch 389/1000, Train Loss: 0.1843, Train Accuracy: 0.9371, Val Loss: 0.1965, Val Accuracy: 0.9280
Epoch 390/1000, Train Loss: 0.1835, Train Accuracy: 0.9366, Val Loss: 0.1960, Val Accuracy: 0.9285
Epoch 391/1000, Train Loss: 0.1845, Train Accuracy: 0.9380, Val Loss: 0.1956, Val Accuracy: 0.9295
Epoch 392/1000, Train Loss: 0.1852, Train Accuracy: 0.9358, Val Loss: 0.1952, Val Accuracy: 0.9290
Epoch 393/1000, Train Loss: 0.1820, Train Accuracy: 0.9380, Val Loss: 0.1947, Val Accuracy: 0.9290
Epoch 394/1000, Train Loss: 0.1809, Train Accuracy: 0.9387, Val Loss: 0.1942, Val Accuracy: 0.9290
Epoch 395/1000, Train Loss: 0.1831, Train Accuracy: 0.9378, Val Loss: 0.1941, Val Accuracy: 0.9280
Epoch 396/1000, Train Loss: 0.1837, Train Accuracy: 0.9378, Val Loss: 0.1934, Val Accuracy: 0.9295
Epoch 397/1000, Train Loss: 0.1802, Train Accuracy: 0.9405, Val Loss: 0.1930, Val Accuracy: 0.9295
Epoch 398/1000, Train Loss: 0.1827, Train Accuracy: 0.9392, Val Loss: 0.1924, Val Accuracy: 0.9295
Epoch 399/1000, Train Loss: 0.1790, Train Accuracy: 0.9359, Val Loss: 0.1919, Val Accuracy: 0.9295
Epoch 400/1000, Train Loss: 0.1799, Train Accuracy: 0.9380, Val Loss: 0.1915, Val Accuracy: 0.9295
Epoch 401/1000, Train Loss: 0.1780, Train Accuracy: 0.9400, Val Loss: 0.1912, Val Accuracy: 0.9295
Epoch 402/1000, Train Loss: 0.1760, Train Accuracy: 0.9398, Val Loss: 0.1908, Val Accuracy: 0.9290
Epoch 403/1000, Train Loss: 0.1792, Train Accuracy: 0.9378, Val Loss: 0.1904, Val Accuracy: 0.9290
Epoch 404/1000, Train Loss: 0.1771, Train Accuracy: 0.9405, Val Loss: 0.1899, Val Accuracy: 0.9295
Epoch 405/1000, Train Loss: 0.1758, Train Accuracy: 0.9394, Val Loss: 0.1893, Val Accuracy: 0.9295
Epoch 406/1000, Train Loss: 0.1766, Train Accuracy: 0.9406, Val Loss: 0.1888, Val Accuracy: 0.9295
Epoch 407/1000, Train Loss: 0.1742, Train Accuracy: 0.9404, Val Loss: 0.1886, Val Accuracy: 0.9295
Epoch 408/1000, Train Loss: 0.1749, Train Accuracy: 0.9412, Val Loss: 0.1882, Val Accuracy: 0.9295
Epoch 409/1000, Train Loss: 0.1773, Train Accuracy: 0.9376, Val Loss: 0.1878, Val Accuracy: 0.9295
Epoch 410/1000, Train Loss: 0.1762, Train Accuracy: 0.9388, Val Loss: 0.1874, Val Accuracy: 0.9295
Epoch 411/1000, Train Loss: 0.1752, Train Accuracy: 0.9397, Val Loss: 0.1871, Val Accuracy: 0.9300
Epoch 412/1000, Train Loss: 0.1751, Train Accuracy: 0.9394, Val Loss: 0.1868, Val Accuracy: 0.9304
Epoch 413/1000, Train Loss: 0.1734, Train Accuracy: 0.9412, Val Loss: 0.1864, Val Accuracy: 0.9304
Epoch 414/1000, Train Loss: 0.1700, Train Accuracy: 0.9421, Val Loss: 0.1858, Val Accuracy: 0.9304
Epoch 415/1000, Train Loss: 0.1714, Train Accuracy: 0.9408, Val Loss: 0.1852, Val Accuracy: 0.9300
Epoch 416/1000, Train Loss: 0.1755, Train Accuracy: 0.9380, Val Loss: 0.1848, Val Accuracy: 0.9300
Epoch 417/1000, Train Loss: 0.1718, Train Accuracy: 0.9393, Val Loss: 0.1844, Val Accuracy: 0.9300
Epoch 418/1000, Train Loss: 0.1695, Train Accuracy: 0.9412, Val Loss: 0.1841, Val Accuracy: 0.9300
Epoch 419/1000, Train Loss: 0.1713, Train Accuracy: 0.9381, Val Loss: 0.1837, Val Accuracy: 0.9300
Epoch 420/1000, Train Loss: 0.1706, Train Accuracy: 0.9417, Val Loss: 0.1834, Val Accuracy: 0.9300
Epoch 421/1000, Train Loss: 0.1722, Train Accuracy: 0.9394, Val Loss: 0.1832, Val Accuracy: 0.9304
Epoch 422/1000, Train Loss: 0.1699, Train Accuracy: 0.9420, Val Loss: 0.1829, Val Accuracy: 0.9300
Epoch 423/1000, Train Loss: 0.1657, Train Accuracy: 0.9420, Val Loss: 0.1825, Val Accuracy: 0.9309
Epoch 424/1000, Train Loss: 0.1688, Train Accuracy: 0.9419, Val Loss: 0.1819, Val Accuracy: 0.9309
Epoch 425/1000, Train Loss: 0.1661, Train Accuracy: 0.9420, Val Loss: 0.1817, Val Accuracy: 0.9314
Epoch 426/1000, Train Loss: 0.1678, Train Accuracy: 0.9415, Val Loss: 0.1814, Val Accuracy: 0.9309
Epoch 427/1000, Train Loss: 0.1659, Train Accuracy: 0.9440, Val Loss: 0.1812, Val Accuracy: 0.9309
Epoch 428/1000, Train Loss: 0.1638, Train Accuracy: 0.9430, Val Loss: 0.1811, Val Accuracy: 0.9304
Epoch 429/1000, Train Loss: 0.1660, Train Accuracy: 0.9445, Val Loss: 0.1805, Val Accuracy: 0.9309
Epoch 430/1000, Train Loss: 0.1677, Train Accuracy: 0.9402, Val Loss: 0.1801, Val Accuracy: 0.9314
Epoch 431/1000, Train Loss: 0.1664, Train Accuracy: 0.9412, Val Loss: 0.1795, Val Accuracy: 0.9314
Epoch 432/1000, Train Loss: 0.1672, Train Accuracy: 0.9392, Val Loss: 0.1791, Val Accuracy: 0.9314
Epoch 433/1000, Train Loss: 0.1658, Train Accuracy: 0.9436, Val Loss: 0.1786, Val Accuracy: 0.9319
Epoch 434/1000, Train Loss: 0.1617, Train Accuracy: 0.9444, Val Loss: 0.1783, Val Accuracy: 0.9314
Epoch 435/1000, Train Loss: 0.1625, Train Accuracy: 0.9454, Val Loss: 0.1781, Val Accuracy: 0.9314
Epoch 436/1000, Train Loss: 0.1645, Train Accuracy: 0.9448, Val Loss: 0.1779, Val Accuracy: 0.9319
Epoch 437/1000, Train Loss: 0.1640, Train Accuracy: 0.9425, Val Loss: 0.1776, Val Accuracy: 0.9319
Epoch 438/1000, Train Loss: 0.1639, Train Accuracy: 0.9430, Val Loss: 0.1773, Val Accuracy: 0.9319
Epoch 439/1000, Train Loss: 0.1616, Train Accuracy: 0.9451, Val Loss: 0.1769, Val Accuracy: 0.9319
Epoch 440/1000, Train Loss: 0.1614, Train Accuracy: 0.9436, Val Loss: 0.1766, Val Accuracy: 0.9319
Epoch 441/1000, Train Loss: 0.1616, Train Accuracy: 0.9459, Val Loss: 0.1764, Val Accuracy: 0.9324
Epoch 442/1000, Train Loss: 0.1589, Train Accuracy: 0.9460, Val Loss: 0.1762, Val Accuracy: 0.9329
Epoch 443/1000, Train Loss: 0.1622, Train Accuracy: 0.9431, Val Loss: 0.1757, Val Accuracy: 0.9329
Epoch 444/1000, Train Loss: 0.1592, Train Accuracy: 0.9449, Val Loss: 0.1754, Val Accuracy: 0.9334
Epoch 445/1000, Train Loss: 0.1604, Train Accuracy: 0.9453, Val Loss: 0.1752, Val Accuracy: 0.9334
Epoch 446/1000, Train Loss: 0.1582, Train Accuracy: 0.9460, Val Loss: 0.1750, Val Accuracy: 0.9334
Epoch 447/1000, Train Loss: 0.1577, Train Accuracy: 0.9440, Val Loss: 0.1747, Val Accuracy: 0.9339
Epoch 448/1000, Train Loss: 0.1558, Train Accuracy: 0.9468, Val Loss: 0.1742, Val Accuracy: 0.9339
Epoch 449/1000, Train Loss: 0.1549, Train Accuracy: 0.9476, Val Loss: 0.1739, Val Accuracy: 0.9343
Epoch 450/1000, Train Loss: 0.1580, Train Accuracy: 0.9455, Val Loss: 0.1737, Val Accuracy: 0.9339
Epoch 451/1000, Train Loss: 0.1553, Train Accuracy: 0.9467, Val Loss: 0.1735, Val Accuracy: 0.9339
Epoch 452/1000, Train Loss: 0.1541, Train Accuracy: 0.9483, Val Loss: 0.1731, Val Accuracy: 0.9343
Epoch 453/1000, Train Loss: 0.1562, Train Accuracy: 0.9476, Val Loss: 0.1726, Val Accuracy: 0.9348
Epoch 454/1000, Train Loss: 0.1538, Train Accuracy: 0.9488, Val Loss: 0.1724, Val Accuracy: 0.9343
Epoch 455/1000, Train Loss: 0.1539, Train Accuracy: 0.9466, Val Loss: 0.1722, Val Accuracy: 0.9348
Epoch 456/1000, Train Loss: 0.1548, Train Accuracy: 0.9455, Val Loss: 0.1719, Val Accuracy: 0.9348
Epoch 457/1000, Train Loss: 0.1549, Train Accuracy: 0.9447, Val Loss: 0.1717, Val Accuracy: 0.9348
Epoch 458/1000, Train Loss: 0.1549, Train Accuracy: 0.9445, Val Loss: 0.1713, Val Accuracy: 0.9353
Epoch 459/1000, Train Loss: 0.1542, Train Accuracy: 0.9464, Val Loss: 0.1710, Val Accuracy: 0.9353
Epoch 460/1000, Train Loss: 0.1498, Train Accuracy: 0.9499, Val Loss: 0.1706, Val Accuracy: 0.9348
Epoch 461/1000, Train Loss: 0.1520, Train Accuracy: 0.9489, Val Loss: 0.1703, Val Accuracy: 0.9348
Epoch 462/1000, Train Loss: 0.1551, Train Accuracy: 0.9460, Val Loss: 0.1700, Val Accuracy: 0.9348
Epoch 463/1000, Train Loss: 0.1519, Train Accuracy: 0.9487, Val Loss: 0.1698, Val Accuracy: 0.9348
Epoch 464/1000, Train Loss: 0.1519, Train Accuracy: 0.9462, Val Loss: 0.1695, Val Accuracy: 0.9363
Epoch 465/1000, Train Loss: 0.1523, Train Accuracy: 0.9482, Val Loss: 0.1694, Val Accuracy: 0.9363
Epoch 466/1000, Train Loss: 0.1520, Train Accuracy: 0.9482, Val Loss: 0.1692, Val Accuracy: 0.9363
Epoch 467/1000, Train Loss: 0.1493, Train Accuracy: 0.9482, Val Loss: 0.1690, Val Accuracy: 0.9363
Epoch 468/1000, Train Loss: 0.1478, Train Accuracy: 0.9502, Val Loss: 0.1687, Val Accuracy: 0.9363
Epoch 469/1000, Train Loss: 0.1495, Train Accuracy: 0.9495, Val Loss: 0.1685, Val Accuracy: 0.9363
Epoch 470/1000, Train Loss: 0.1509, Train Accuracy: 0.9470, Val Loss: 0.1681, Val Accuracy: 0.9363
Epoch 471/1000, Train Loss: 0.1500, Train Accuracy: 0.9473, Val Loss: 0.1678, Val Accuracy: 0.9358
Epoch 472/1000, Train Loss: 0.1465, Train Accuracy: 0.9493, Val Loss: 0.1673, Val Accuracy: 0.9358
Epoch 473/1000, Train Loss: 0.1466, Train Accuracy: 0.9498, Val Loss: 0.1671, Val Accuracy: 0.9358
Epoch 474/1000, Train Loss: 0.1479, Train Accuracy: 0.9499, Val Loss: 0.1669, Val Accuracy: 0.9358
Epoch 475/1000, Train Loss: 0.1449, Train Accuracy: 0.9488, Val Loss: 0.1664, Val Accuracy: 0.9358
Epoch 476/1000, Train Loss: 0.1488, Train Accuracy: 0.9465, Val Loss: 0.1661, Val Accuracy: 0.9358
Epoch 477/1000, Train Loss: 0.1473, Train Accuracy: 0.9485, Val Loss: 0.1658, Val Accuracy: 0.9358
Epoch 478/1000, Train Loss: 0.1466, Train Accuracy: 0.9499, Val Loss: 0.1655, Val Accuracy: 0.9353
Epoch 479/1000, Train Loss: 0.1444, Train Accuracy: 0.9511, Val Loss: 0.1653, Val Accuracy: 0.9353
Epoch 480/1000, Train Loss: 0.1470, Train Accuracy: 0.9492, Val Loss: 0.1652, Val Accuracy: 0.9353
Epoch 481/1000, Train Loss: 0.1453, Train Accuracy: 0.9513, Val Loss: 0.1651, Val Accuracy: 0.9358
Epoch 482/1000, Train Loss: 0.1459, Train Accuracy: 0.9504, Val Loss: 0.1643, Val Accuracy: 0.9353
Epoch 483/1000, Train Loss: 0.1457, Train Accuracy: 0.9493, Val Loss: 0.1644, Val Accuracy: 0.9353
Epoch 484/1000, Train Loss: 0.1454, Train Accuracy: 0.9477, Val Loss: 0.1641, Val Accuracy: 0.9353
Epoch 485/1000, Train Loss: 0.1465, Train Accuracy: 0.9495, Val Loss: 0.1638, Val Accuracy: 0.9353
Epoch 486/1000, Train Loss: 0.1428, Train Accuracy: 0.9505, Val Loss: 0.1636, Val Accuracy: 0.9353
Epoch 487/1000, Train Loss: 0.1418, Train Accuracy: 0.9515, Val Loss: 0.1634, Val Accuracy: 0.9353
Epoch 488/1000, Train Loss: 0.1447, Train Accuracy: 0.9496, Val Loss: 0.1631, Val Accuracy: 0.9353
Epoch 489/1000, Train Loss: 0.1421, Train Accuracy: 0.9506, Val Loss: 0.1627, Val Accuracy: 0.9353
Epoch 490/1000, Train Loss: 0.1436, Train Accuracy: 0.9507, Val Loss: 0.1626, Val Accuracy: 0.9353
Epoch 491/1000, Train Loss: 0.1389, Train Accuracy: 0.9529, Val Loss: 0.1626, Val Accuracy: 0.9353
Epoch 492/1000, Train Loss: 0.1391, Train Accuracy: 0.9523, Val Loss: 0.1626, Val Accuracy: 0.9353
Epoch 493/1000, Train Loss: 0.1404, Train Accuracy: 0.9511, Val Loss: 0.1622, Val Accuracy: 0.9353
Epoch 494/1000, Train Loss: 0.1408, Train Accuracy: 0.9511, Val Loss: 0.1620, Val Accuracy: 0.9353
Epoch 495/1000, Train Loss: 0.1358, Train Accuracy: 0.9535, Val Loss: 0.1618, Val Accuracy: 0.9353
Epoch 496/1000, Train Loss: 0.1394, Train Accuracy: 0.9546, Val Loss: 0.1615, Val Accuracy: 0.9353
Epoch 497/1000, Train Loss: 0.1400, Train Accuracy: 0.9541, Val Loss: 0.1612, Val Accuracy: 0.9353
Epoch 498/1000, Train Loss: 0.1401, Train Accuracy: 0.9513, Val Loss: 0.1609, Val Accuracy: 0.9353
Epoch 499/1000, Train Loss: 0.1406, Train Accuracy: 0.9502, Val Loss: 0.1608, Val Accuracy: 0.9353
Epoch 500/1000, Train Loss: 0.1389, Train Accuracy: 0.9518, Val Loss: 0.1605, Val Accuracy: 0.9358
Epoch 501/1000, Train Loss: 0.1377, Train Accuracy: 0.9522, Val Loss: 0.1604, Val Accuracy: 0.9358
Epoch 502/1000, Train Loss: 0.1368, Train Accuracy: 0.9544, Val Loss: 0.1602, Val Accuracy: 0.9363
Epoch 503/1000, Train Loss: 0.1360, Train Accuracy: 0.9543, Val Loss: 0.1598, Val Accuracy: 0.9363
Epoch 504/1000, Train Loss: 0.1376, Train Accuracy: 0.9511, Val Loss: 0.1597, Val Accuracy: 0.9363
Epoch 505/1000, Train Loss: 0.1325, Train Accuracy: 0.9577, Val Loss: 0.1596, Val Accuracy: 0.9368
Epoch 506/1000, Train Loss: 0.1372, Train Accuracy: 0.9521, Val Loss: 0.1592, Val Accuracy: 0.9373
Epoch 507/1000, Train Loss: 0.1371, Train Accuracy: 0.9527, Val Loss: 0.1591, Val Accuracy: 0.9377
Epoch 508/1000, Train Loss: 0.1342, Train Accuracy: 0.9532, Val Loss: 0.1587, Val Accuracy: 0.9377
Epoch 509/1000, Train Loss: 0.1352, Train Accuracy: 0.9544, Val Loss: 0.1585, Val Accuracy: 0.9377
Epoch 510/1000, Train Loss: 0.1336, Train Accuracy: 0.9548, Val Loss: 0.1584, Val Accuracy: 0.9373
Epoch 511/1000, Train Loss: 0.1371, Train Accuracy: 0.9522, Val Loss: 0.1582, Val Accuracy: 0.9377
Epoch 512/1000, Train Loss: 0.1336, Train Accuracy: 0.9543, Val Loss: 0.1580, Val Accuracy: 0.9377
Epoch 513/1000, Train Loss: 0.1334, Train Accuracy: 0.9567, Val Loss: 0.1578, Val Accuracy: 0.9382
Epoch 514/1000, Train Loss: 0.1345, Train Accuracy: 0.9540, Val Loss: 0.1579, Val Accuracy: 0.9377
Epoch 515/1000, Train Loss: 0.1355, Train Accuracy: 0.9533, Val Loss: 0.1574, Val Accuracy: 0.9377
Epoch 516/1000, Train Loss: 0.1297, Train Accuracy: 0.9546, Val Loss: 0.1572, Val Accuracy: 0.9377
Epoch 517/1000, Train Loss: 0.1313, Train Accuracy: 0.9520, Val Loss: 0.1568, Val Accuracy: 0.9377
Epoch 518/1000, Train Loss: 0.1333, Train Accuracy: 0.9530, Val Loss: 0.1568, Val Accuracy: 0.9377
Epoch 519/1000, Train Loss: 0.1283, Train Accuracy: 0.9589, Val Loss: 0.1565, Val Accuracy: 0.9377
Epoch 520/1000, Train Loss: 0.1308, Train Accuracy: 0.9560, Val Loss: 0.1564, Val Accuracy: 0.9382
Epoch 521/1000, Train Loss: 0.1323, Train Accuracy: 0.9560, Val Loss: 0.1559, Val Accuracy: 0.9382
Epoch 522/1000, Train Loss: 0.1328, Train Accuracy: 0.9537, Val Loss: 0.1555, Val Accuracy: 0.9392
Epoch 523/1000, Train Loss: 0.1312, Train Accuracy: 0.9557, Val Loss: 0.1553, Val Accuracy: 0.9392
Epoch 524/1000, Train Loss: 0.1342, Train Accuracy: 0.9518, Val Loss: 0.1552, Val Accuracy: 0.9392
Epoch 525/1000, Train Loss: 0.1309, Train Accuracy: 0.9557, Val Loss: 0.1551, Val Accuracy: 0.9387
Epoch 526/1000, Train Loss: 0.1289, Train Accuracy: 0.9565, Val Loss: 0.1550, Val Accuracy: 0.9387
Epoch 527/1000, Train Loss: 0.1315, Train Accuracy: 0.9545, Val Loss: 0.1549, Val Accuracy: 0.9387
Epoch 528/1000, Train Loss: 0.1295, Train Accuracy: 0.9573, Val Loss: 0.1546, Val Accuracy: 0.9387
Epoch 529/1000, Train Loss: 0.1294, Train Accuracy: 0.9574, Val Loss: 0.1547, Val Accuracy: 0.9382
Epoch 530/1000, Train Loss: 0.1293, Train Accuracy: 0.9555, Val Loss: 0.1544, Val Accuracy: 0.9387
Epoch 531/1000, Train Loss: 0.1294, Train Accuracy: 0.9571, Val Loss: 0.1544, Val Accuracy: 0.9392
Epoch 532/1000, Train Loss: 0.1276, Train Accuracy: 0.9575, Val Loss: 0.1539, Val Accuracy: 0.9397
Epoch 533/1000, Train Loss: 0.1285, Train Accuracy: 0.9562, Val Loss: 0.1537, Val Accuracy: 0.9397
Epoch 534/1000, Train Loss: 0.1274, Train Accuracy: 0.9578, Val Loss: 0.1536, Val Accuracy: 0.9402
Epoch 535/1000, Train Loss: 0.1251, Train Accuracy: 0.9580, Val Loss: 0.1534, Val Accuracy: 0.9407
Epoch 536/1000, Train Loss: 0.1262, Train Accuracy: 0.9578, Val Loss: 0.1533, Val Accuracy: 0.9397
Epoch 537/1000, Train Loss: 0.1282, Train Accuracy: 0.9551, Val Loss: 0.1532, Val Accuracy: 0.9397
Epoch 538/1000, Train Loss: 0.1284, Train Accuracy: 0.9588, Val Loss: 0.1534, Val Accuracy: 0.9397
Epoch 539/1000, Train Loss: 0.1268, Train Accuracy: 0.9556, Val Loss: 0.1531, Val Accuracy: 0.9397
Epoch 540/1000, Train Loss: 0.1255, Train Accuracy: 0.9572, Val Loss: 0.1527, Val Accuracy: 0.9397
Epoch 541/1000, Train Loss: 0.1258, Train Accuracy: 0.9590, Val Loss: 0.1526, Val Accuracy: 0.9402
Epoch 542/1000, Train Loss: 0.1283, Train Accuracy: 0.9563, Val Loss: 0.1523, Val Accuracy: 0.9407
Epoch 543/1000, Train Loss: 0.1228, Train Accuracy: 0.9585, Val Loss: 0.1521, Val Accuracy: 0.9402
Epoch 544/1000, Train Loss: 0.1264, Train Accuracy: 0.9579, Val Loss: 0.1519, Val Accuracy: 0.9407
Epoch 545/1000, Train Loss: 0.1220, Train Accuracy: 0.9586, Val Loss: 0.1517, Val Accuracy: 0.9411
Epoch 546/1000, Train Loss: 0.1266, Train Accuracy: 0.9589, Val Loss: 0.1514, Val Accuracy: 0.9411
Epoch 547/1000, Train Loss: 0.1227, Train Accuracy: 0.9582, Val Loss: 0.1511, Val Accuracy: 0.9411
Epoch 548/1000, Train Loss: 0.1241, Train Accuracy: 0.9555, Val Loss: 0.1509, Val Accuracy: 0.9416
Epoch 549/1000, Train Loss: 0.1226, Train Accuracy: 0.9586, Val Loss: 0.1512, Val Accuracy: 0.9407
Epoch 550/1000, Train Loss: 0.1204, Train Accuracy: 0.9600, Val Loss: 0.1510, Val Accuracy: 0.9407
Epoch 551/1000, Train Loss: 0.1245, Train Accuracy: 0.9574, Val Loss: 0.1509, Val Accuracy: 0.9402
Epoch 552/1000, Train Loss: 0.1232, Train Accuracy: 0.9565, Val Loss: 0.1505, Val Accuracy: 0.9407
Epoch 553/1000, Train Loss: 0.1201, Train Accuracy: 0.9583, Val Loss: 0.1504, Val Accuracy: 0.9407
Epoch 554/1000, Train Loss: 0.1215, Train Accuracy: 0.9579, Val Loss: 0.1504, Val Accuracy: 0.9407
Epoch 555/1000, Train Loss: 0.1208, Train Accuracy: 0.9600, Val Loss: 0.1501, Val Accuracy: 0.9411
Epoch 556/1000, Train Loss: 0.1219, Train Accuracy: 0.9584, Val Loss: 0.1499, Val Accuracy: 0.9411
Epoch 557/1000, Train Loss: 0.1245, Train Accuracy: 0.9580, Val Loss: 0.1498, Val Accuracy: 0.9407
Epoch 558/1000, Train Loss: 0.1231, Train Accuracy: 0.9574, Val Loss: 0.1496, Val Accuracy: 0.9407
Epoch 559/1000, Train Loss: 0.1202, Train Accuracy: 0.9585, Val Loss: 0.1495, Val Accuracy: 0.9402
Epoch 560/1000, Train Loss: 0.1174, Train Accuracy: 0.9610, Val Loss: 0.1491, Val Accuracy: 0.9407
Epoch 561/1000, Train Loss: 0.1197, Train Accuracy: 0.9611, Val Loss: 0.1488, Val Accuracy: 0.9411
Epoch 562/1000, Train Loss: 0.1189, Train Accuracy: 0.9590, Val Loss: 0.1486, Val Accuracy: 0.9411
Epoch 563/1000, Train Loss: 0.1193, Train Accuracy: 0.9593, Val Loss: 0.1484, Val Accuracy: 0.9411
Epoch 564/1000, Train Loss: 0.1163, Train Accuracy: 0.9620, Val Loss: 0.1481, Val Accuracy: 0.9416
Epoch 565/1000, Train Loss: 0.1176, Train Accuracy: 0.9597, Val Loss: 0.1482, Val Accuracy: 0.9416
Epoch 566/1000, Train Loss: 0.1178, Train Accuracy: 0.9619, Val Loss: 0.1478, Val Accuracy: 0.9416
Epoch 567/1000, Train Loss: 0.1154, Train Accuracy: 0.9595, Val Loss: 0.1479, Val Accuracy: 0.9411
Epoch 568/1000, Train Loss: 0.1192, Train Accuracy: 0.9580, Val Loss: 0.1478, Val Accuracy: 0.9411
Epoch 569/1000, Train Loss: 0.1188, Train Accuracy: 0.9611, Val Loss: 0.1478, Val Accuracy: 0.9411
Epoch 570/1000, Train Loss: 0.1161, Train Accuracy: 0.9603, Val Loss: 0.1478, Val Accuracy: 0.9411
Epoch 571/1000, Train Loss: 0.1145, Train Accuracy: 0.9608, Val Loss: 0.1475, Val Accuracy: 0.9411
Epoch 572/1000, Train Loss: 0.1168, Train Accuracy: 0.9594, Val Loss: 0.1476, Val Accuracy: 0.9411
Epoch 573/1000, Train Loss: 0.1175, Train Accuracy: 0.9601, Val Loss: 0.1474, Val Accuracy: 0.9407
Epoch 574/1000, Train Loss: 0.1150, Train Accuracy: 0.9619, Val Loss: 0.1472, Val Accuracy: 0.9407
Epoch 575/1000, Train Loss: 0.1164, Train Accuracy: 0.9596, Val Loss: 0.1468, Val Accuracy: 0.9421
Epoch 576/1000, Train Loss: 0.1172, Train Accuracy: 0.9614, Val Loss: 0.1467, Val Accuracy: 0.9421
Epoch 577/1000, Train Loss: 0.1162, Train Accuracy: 0.9614, Val Loss: 0.1465, Val Accuracy: 0.9421
Epoch 578/1000, Train Loss: 0.1131, Train Accuracy: 0.9629, Val Loss: 0.1463, Val Accuracy: 0.9421
Epoch 579/1000, Train Loss: 0.1171, Train Accuracy: 0.9595, Val Loss: 0.1461, Val Accuracy: 0.9421
Epoch 580/1000, Train Loss: 0.1169, Train Accuracy: 0.9601, Val Loss: 0.1459, Val Accuracy: 0.9416
Epoch 581/1000, Train Loss: 0.1167, Train Accuracy: 0.9600, Val Loss: 0.1457, Val Accuracy: 0.9416
Epoch 582/1000, Train Loss: 0.1142, Train Accuracy: 0.9597, Val Loss: 0.1458, Val Accuracy: 0.9416
Epoch 583/1000, Train Loss: 0.1152, Train Accuracy: 0.9612, Val Loss: 0.1456, Val Accuracy: 0.9421
Epoch 584/1000, Train Loss: 0.1139, Train Accuracy: 0.9611, Val Loss: 0.1456, Val Accuracy: 0.9426
Epoch 585/1000, Train Loss: 0.1105, Train Accuracy: 0.9629, Val Loss: 0.1456, Val Accuracy: 0.9421
Epoch 586/1000, Train Loss: 0.1096, Train Accuracy: 0.9629, Val Loss: 0.1454, Val Accuracy: 0.9426
Epoch 587/1000, Train Loss: 0.1120, Train Accuracy: 0.9634, Val Loss: 0.1453, Val Accuracy: 0.9426
Epoch 588/1000, Train Loss: 0.1141, Train Accuracy: 0.9610, Val Loss: 0.1453, Val Accuracy: 0.9426
Epoch 589/1000, Train Loss: 0.1116, Train Accuracy: 0.9627, Val Loss: 0.1452, Val Accuracy: 0.9426
Epoch 590/1000, Train Loss: 0.1105, Train Accuracy: 0.9640, Val Loss: 0.1452, Val Accuracy: 0.9426
Epoch 591/1000, Train Loss: 0.1118, Train Accuracy: 0.9641, Val Loss: 0.1450, Val Accuracy: 0.9431
Epoch 592/1000, Train Loss: 0.1103, Train Accuracy: 0.9630, Val Loss: 0.1448, Val Accuracy: 0.9431
Epoch 593/1000, Train Loss: 0.1106, Train Accuracy: 0.9633, Val Loss: 0.1446, Val Accuracy: 0.9436
Epoch 594/1000, Train Loss: 0.1087, Train Accuracy: 0.9647, Val Loss: 0.1446, Val Accuracy: 0.9436
Epoch 595/1000, Train Loss: 0.1084, Train Accuracy: 0.9624, Val Loss: 0.1442, Val Accuracy: 0.9436
Epoch 596/1000, Train Loss: 0.1059, Train Accuracy: 0.9655, Val Loss: 0.1439, Val Accuracy: 0.9431
Epoch 597/1000, Train Loss: 0.1086, Train Accuracy: 0.9641, Val Loss: 0.1437, Val Accuracy: 0.9436
Epoch 598/1000, Train Loss: 0.1085, Train Accuracy: 0.9631, Val Loss: 0.1435, Val Accuracy: 0.9436
Epoch 599/1000, Train Loss: 0.1117, Train Accuracy: 0.9638, Val Loss: 0.1433, Val Accuracy: 0.9436
Epoch 600/1000, Train Loss: 0.1085, Train Accuracy: 0.9644, Val Loss: 0.1430, Val Accuracy: 0.9436
Epoch 601/1000, Train Loss: 0.1099, Train Accuracy: 0.9630, Val Loss: 0.1431, Val Accuracy: 0.9436
Epoch 602/1000, Train Loss: 0.1069, Train Accuracy: 0.9647, Val Loss: 0.1430, Val Accuracy: 0.9431
Epoch 603/1000, Train Loss: 0.1072, Train Accuracy: 0.9631, Val Loss: 0.1431, Val Accuracy: 0.9431
Epoch 604/1000, Train Loss: 0.1106, Train Accuracy: 0.9612, Val Loss: 0.1431, Val Accuracy: 0.9431
Epoch 605/1000, Train Loss: 0.1052, Train Accuracy: 0.9648, Val Loss: 0.1426, Val Accuracy: 0.9436
Epoch 606/1000, Train Loss: 0.1064, Train Accuracy: 0.9644, Val Loss: 0.1425, Val Accuracy: 0.9441
Epoch 607/1000, Train Loss: 0.1072, Train Accuracy: 0.9625, Val Loss: 0.1424, Val Accuracy: 0.9436
Epoch 608/1000, Train Loss: 0.1070, Train Accuracy: 0.9638, Val Loss: 0.1423, Val Accuracy: 0.9436
Epoch 609/1000, Train Loss: 0.1064, Train Accuracy: 0.9652, Val Loss: 0.1422, Val Accuracy: 0.9436
Epoch 610/1000, Train Loss: 0.1044, Train Accuracy: 0.9653, Val Loss: 0.1419, Val Accuracy: 0.9436
Epoch 611/1000, Train Loss: 0.1054, Train Accuracy: 0.9656, Val Loss: 0.1416, Val Accuracy: 0.9450
Epoch 612/1000, Train Loss: 0.1059, Train Accuracy: 0.9645, Val Loss: 0.1413, Val Accuracy: 0.9460
Epoch 613/1000, Train Loss: 0.1072, Train Accuracy: 0.9639, Val Loss: 0.1415, Val Accuracy: 0.9441
Epoch 614/1000, Train Loss: 0.1063, Train Accuracy: 0.9642, Val Loss: 0.1415, Val Accuracy: 0.9441
Epoch 615/1000, Train Loss: 0.1045, Train Accuracy: 0.9653, Val Loss: 0.1413, Val Accuracy: 0.9446
Epoch 616/1000, Train Loss: 0.1072, Train Accuracy: 0.9634, Val Loss: 0.1412, Val Accuracy: 0.9450
Epoch 617/1000, Train Loss: 0.1023, Train Accuracy: 0.9664, Val Loss: 0.1412, Val Accuracy: 0.9446
Epoch 618/1000, Train Loss: 0.1044, Train Accuracy: 0.9650, Val Loss: 0.1409, Val Accuracy: 0.9450
Epoch 619/1000, Train Loss: 0.1025, Train Accuracy: 0.9675, Val Loss: 0.1409, Val Accuracy: 0.9446
Epoch 620/1000, Train Loss: 0.1011, Train Accuracy: 0.9676, Val Loss: 0.1407, Val Accuracy: 0.9465
Epoch 621/1000, Train Loss: 0.1035, Train Accuracy: 0.9635, Val Loss: 0.1408, Val Accuracy: 0.9460
Epoch 622/1000, Train Loss: 0.1044, Train Accuracy: 0.9651, Val Loss: 0.1409, Val Accuracy: 0.9460
Epoch 623/1000, Train Loss: 0.1009, Train Accuracy: 0.9667, Val Loss: 0.1406, Val Accuracy: 0.9455
Epoch 624/1000, Train Loss: 0.1025, Train Accuracy: 0.9665, Val Loss: 0.1404, Val Accuracy: 0.9455
Epoch 625/1000, Train Loss: 0.1038, Train Accuracy: 0.9664, Val Loss: 0.1401, Val Accuracy: 0.9470
Epoch 626/1000, Train Loss: 0.1008, Train Accuracy: 0.9668, Val Loss: 0.1400, Val Accuracy: 0.9465
Epoch 627/1000, Train Loss: 0.1039, Train Accuracy: 0.9656, Val Loss: 0.1402, Val Accuracy: 0.9460
Epoch 628/1000, Train Loss: 0.1011, Train Accuracy: 0.9672, Val Loss: 0.1402, Val Accuracy: 0.9460
Epoch 629/1000, Train Loss: 0.0978, Train Accuracy: 0.9690, Val Loss: 0.1399, Val Accuracy: 0.9465
Epoch 630/1000, Train Loss: 0.1010, Train Accuracy: 0.9650, Val Loss: 0.1398, Val Accuracy: 0.9465
Epoch 631/1000, Train Loss: 0.1026, Train Accuracy: 0.9665, Val Loss: 0.1396, Val Accuracy: 0.9470
Epoch 632/1000, Train Loss: 0.1026, Train Accuracy: 0.9676, Val Loss: 0.1396, Val Accuracy: 0.9460
Epoch 633/1000, Train Loss: 0.1003, Train Accuracy: 0.9656, Val Loss: 0.1393, Val Accuracy: 0.9470
Epoch 634/1000, Train Loss: 0.0996, Train Accuracy: 0.9680, Val Loss: 0.1393, Val Accuracy: 0.9470
Epoch 635/1000, Train Loss: 0.1026, Train Accuracy: 0.9655, Val Loss: 0.1393, Val Accuracy: 0.9465
Epoch 636/1000, Train Loss: 0.1020, Train Accuracy: 0.9659, Val Loss: 0.1393, Val Accuracy: 0.9465
Epoch 637/1000, Train Loss: 0.1025, Train Accuracy: 0.9661, Val Loss: 0.1391, Val Accuracy: 0.9470
Epoch 638/1000, Train Loss: 0.0985, Train Accuracy: 0.9662, Val Loss: 0.1389, Val Accuracy: 0.9470
Epoch 639/1000, Train Loss: 0.0989, Train Accuracy: 0.9670, Val Loss: 0.1389, Val Accuracy: 0.9470
Epoch 640/1000, Train Loss: 0.1001, Train Accuracy: 0.9653, Val Loss: 0.1387, Val Accuracy: 0.9470
Epoch 641/1000, Train Loss: 0.0973, Train Accuracy: 0.9681, Val Loss: 0.1385, Val Accuracy: 0.9470
Epoch 642/1000, Train Loss: 0.0967, Train Accuracy: 0.9683, Val Loss: 0.1385, Val Accuracy: 0.9470
Epoch 643/1000, Train Loss: 0.0977, Train Accuracy: 0.9683, Val Loss: 0.1385, Val Accuracy: 0.9470
Epoch 644/1000, Train Loss: 0.0981, Train Accuracy: 0.9664, Val Loss: 0.1381, Val Accuracy: 0.9470
Epoch 645/1000, Train Loss: 0.0949, Train Accuracy: 0.9691, Val Loss: 0.1380, Val Accuracy: 0.9470
Epoch 646/1000, Train Loss: 0.0977, Train Accuracy: 0.9690, Val Loss: 0.1381, Val Accuracy: 0.9470
Epoch 647/1000, Train Loss: 0.0954, Train Accuracy: 0.9673, Val Loss: 0.1379, Val Accuracy: 0.9470
Epoch 648/1000, Train Loss: 0.0961, Train Accuracy: 0.9684, Val Loss: 0.1381, Val Accuracy: 0.9470
Epoch 649/1000, Train Loss: 0.0971, Train Accuracy: 0.9675, Val Loss: 0.1381, Val Accuracy: 0.9470
Epoch 650/1000, Train Loss: 0.0936, Train Accuracy: 0.9707, Val Loss: 0.1379, Val Accuracy: 0.9470
Epoch 651/1000, Train Loss: 0.0975, Train Accuracy: 0.9678, Val Loss: 0.1375, Val Accuracy: 0.9470
Epoch 652/1000, Train Loss: 0.0961, Train Accuracy: 0.9669, Val Loss: 0.1376, Val Accuracy: 0.9470
Epoch 653/1000, Train Loss: 0.0978, Train Accuracy: 0.9678, Val Loss: 0.1374, Val Accuracy: 0.9470
Epoch 654/1000, Train Loss: 0.0946, Train Accuracy: 0.9706, Val Loss: 0.1372, Val Accuracy: 0.9475
Epoch 655/1000, Train Loss: 0.0966, Train Accuracy: 0.9673, Val Loss: 0.1371, Val Accuracy: 0.9470
Epoch 656/1000, Train Loss: 0.0941, Train Accuracy: 0.9700, Val Loss: 0.1372, Val Accuracy: 0.9470
Epoch 657/1000, Train Loss: 0.0962, Train Accuracy: 0.9680, Val Loss: 0.1373, Val Accuracy: 0.9470
Epoch 658/1000, Train Loss: 0.0924, Train Accuracy: 0.9697, Val Loss: 0.1370, Val Accuracy: 0.9475
Epoch 659/1000, Train Loss: 0.0959, Train Accuracy: 0.9683, Val Loss: 0.1369, Val Accuracy: 0.9475
Epoch 660/1000, Train Loss: 0.0935, Train Accuracy: 0.9693, Val Loss: 0.1368, Val Accuracy: 0.9480
Epoch 661/1000, Train Loss: 0.0942, Train Accuracy: 0.9700, Val Loss: 0.1367, Val Accuracy: 0.9480
Epoch 662/1000, Train Loss: 0.0954, Train Accuracy: 0.9685, Val Loss: 0.1366, Val Accuracy: 0.9480
Epoch 663/1000, Train Loss: 0.0949, Train Accuracy: 0.9673, Val Loss: 0.1364, Val Accuracy: 0.9480
Epoch 664/1000, Train Loss: 0.0917, Train Accuracy: 0.9706, Val Loss: 0.1365, Val Accuracy: 0.9480
Epoch 665/1000, Train Loss: 0.0940, Train Accuracy: 0.9687, Val Loss: 0.1366, Val Accuracy: 0.9480
Epoch 666/1000, Train Loss: 0.0948, Train Accuracy: 0.9673, Val Loss: 0.1365, Val Accuracy: 0.9480
Epoch 667/1000, Train Loss: 0.0935, Train Accuracy: 0.9679, Val Loss: 0.1364, Val Accuracy: 0.9480
Epoch 668/1000, Train Loss: 0.0938, Train Accuracy: 0.9693, Val Loss: 0.1361, Val Accuracy: 0.9480
Epoch 669/1000, Train Loss: 0.0906, Train Accuracy: 0.9685, Val Loss: 0.1362, Val Accuracy: 0.9480
Epoch 670/1000, Train Loss: 0.0906, Train Accuracy: 0.9708, Val Loss: 0.1361, Val Accuracy: 0.9480
Epoch 671/1000, Train Loss: 0.0914, Train Accuracy: 0.9692, Val Loss: 0.1361, Val Accuracy: 0.9480
Epoch 672/1000, Train Loss: 0.0927, Train Accuracy: 0.9698, Val Loss: 0.1360, Val Accuracy: 0.9480
Epoch 673/1000, Train Loss: 0.0886, Train Accuracy: 0.9732, Val Loss: 0.1357, Val Accuracy: 0.9480
Epoch 674/1000, Train Loss: 0.0918, Train Accuracy: 0.9704, Val Loss: 0.1355, Val Accuracy: 0.9480
Epoch 675/1000, Train Loss: 0.0909, Train Accuracy: 0.9704, Val Loss: 0.1355, Val Accuracy: 0.9480
Epoch 676/1000, Train Loss: 0.0931, Train Accuracy: 0.9685, Val Loss: 0.1354, Val Accuracy: 0.9480
Epoch 677/1000, Train Loss: 0.0911, Train Accuracy: 0.9697, Val Loss: 0.1354, Val Accuracy: 0.9480
Epoch 678/1000, Train Loss: 0.0902, Train Accuracy: 0.9712, Val Loss: 0.1355, Val Accuracy: 0.9480
Epoch 679/1000, Train Loss: 0.0881, Train Accuracy: 0.9718, Val Loss: 0.1354, Val Accuracy: 0.9480
Epoch 680/1000, Train Loss: 0.0906, Train Accuracy: 0.9698, Val Loss: 0.1352, Val Accuracy: 0.9480
Epoch 681/1000, Train Loss: 0.0906, Train Accuracy: 0.9695, Val Loss: 0.1349, Val Accuracy: 0.9484
Epoch 682/1000, Train Loss: 0.0887, Train Accuracy: 0.9725, Val Loss: 0.1349, Val Accuracy: 0.9480
Epoch 683/1000, Train Loss: 0.0904, Train Accuracy: 0.9704, Val Loss: 0.1350, Val Accuracy: 0.9480
Epoch 684/1000, Train Loss: 0.0891, Train Accuracy: 0.9721, Val Loss: 0.1348, Val Accuracy: 0.9480
Epoch 685/1000, Train Loss: 0.0881, Train Accuracy: 0.9702, Val Loss: 0.1345, Val Accuracy: 0.9484
Epoch 686/1000, Train Loss: 0.0848, Train Accuracy: 0.9743, Val Loss: 0.1344, Val Accuracy: 0.9484
Epoch 687/1000, Train Loss: 0.0882, Train Accuracy: 0.9725, Val Loss: 0.1345, Val Accuracy: 0.9484
Epoch 688/1000, Train Loss: 0.0885, Train Accuracy: 0.9703, Val Loss: 0.1345, Val Accuracy: 0.9480
Epoch 689/1000, Train Loss: 0.0883, Train Accuracy: 0.9710, Val Loss: 0.1347, Val Accuracy: 0.9480
Epoch 690/1000, Train Loss: 0.0878, Train Accuracy: 0.9718, Val Loss: 0.1346, Val Accuracy: 0.9480
Epoch 691/1000, Train Loss: 0.0871, Train Accuracy: 0.9729, Val Loss: 0.1345, Val Accuracy: 0.9484
Epoch 692/1000, Train Loss: 0.0884, Train Accuracy: 0.9718, Val Loss: 0.1342, Val Accuracy: 0.9484
Epoch 693/1000, Train Loss: 0.0871, Train Accuracy: 0.9729, Val Loss: 0.1341, Val Accuracy: 0.9484
Epoch 694/1000, Train Loss: 0.0855, Train Accuracy: 0.9720, Val Loss: 0.1342, Val Accuracy: 0.9484
Epoch 695/1000, Train Loss: 0.0837, Train Accuracy: 0.9756, Val Loss: 0.1338, Val Accuracy: 0.9484
Epoch 696/1000, Train Loss: 0.0874, Train Accuracy: 0.9710, Val Loss: 0.1340, Val Accuracy: 0.9484
Epoch 697/1000, Train Loss: 0.0855, Train Accuracy: 0.9724, Val Loss: 0.1339, Val Accuracy: 0.9484
Epoch 698/1000, Train Loss: 0.0838, Train Accuracy: 0.9745, Val Loss: 0.1337, Val Accuracy: 0.9484
Epoch 699/1000, Train Loss: 0.0851, Train Accuracy: 0.9724, Val Loss: 0.1338, Val Accuracy: 0.9484
Epoch 700/1000, Train Loss: 0.0879, Train Accuracy: 0.9719, Val Loss: 0.1337, Val Accuracy: 0.9484
Epoch 701/1000, Train Loss: 0.0853, Train Accuracy: 0.9732, Val Loss: 0.1336, Val Accuracy: 0.9484
Epoch 702/1000, Train Loss: 0.0841, Train Accuracy: 0.9741, Val Loss: 0.1335, Val Accuracy: 0.9484
Epoch 703/1000, Train Loss: 0.0871, Train Accuracy: 0.9704, Val Loss: 0.1335, Val Accuracy: 0.9484
Epoch 704/1000, Train Loss: 0.0843, Train Accuracy: 0.9718, Val Loss: 0.1334, Val Accuracy: 0.9484
Epoch 705/1000, Train Loss: 0.0855, Train Accuracy: 0.9731, Val Loss: 0.1331, Val Accuracy: 0.9484
Epoch 706/1000, Train Loss: 0.0846, Train Accuracy: 0.9731, Val Loss: 0.1330, Val Accuracy: 0.9484
Epoch 707/1000, Train Loss: 0.0836, Train Accuracy: 0.9738, Val Loss: 0.1330, Val Accuracy: 0.9484
Epoch 708/1000, Train Loss: 0.0838, Train Accuracy: 0.9728, Val Loss: 0.1330, Val Accuracy: 0.9484
Epoch 709/1000, Train Loss: 0.0839, Train Accuracy: 0.9730, Val Loss: 0.1330, Val Accuracy: 0.9484
Epoch 710/1000, Train Loss: 0.0830, Train Accuracy: 0.9746, Val Loss: 0.1329, Val Accuracy: 0.9489
Epoch 711/1000, Train Loss: 0.0795, Train Accuracy: 0.9751, Val Loss: 0.1329, Val Accuracy: 0.9484
Epoch 712/1000, Train Loss: 0.0824, Train Accuracy: 0.9738, Val Loss: 0.1328, Val Accuracy: 0.9484
Epoch 713/1000, Train Loss: 0.0823, Train Accuracy: 0.9725, Val Loss: 0.1329, Val Accuracy: 0.9475
Epoch 714/1000, Train Loss: 0.0840, Train Accuracy: 0.9730, Val Loss: 0.1327, Val Accuracy: 0.9484
Epoch 715/1000, Train Loss: 0.0829, Train Accuracy: 0.9737, Val Loss: 0.1327, Val Accuracy: 0.9484
Epoch 716/1000, Train Loss: 0.0827, Train Accuracy: 0.9724, Val Loss: 0.1325, Val Accuracy: 0.9480
Epoch 717/1000, Train Loss: 0.0848, Train Accuracy: 0.9718, Val Loss: 0.1325, Val Accuracy: 0.9480
Epoch 718/1000, Train Loss: 0.0807, Train Accuracy: 0.9747, Val Loss: 0.1326, Val Accuracy: 0.9480
Epoch 719/1000, Train Loss: 0.0835, Train Accuracy: 0.9730, Val Loss: 0.1325, Val Accuracy: 0.9484
Epoch 720/1000, Train Loss: 0.0798, Train Accuracy: 0.9747, Val Loss: 0.1325, Val Accuracy: 0.9484
Epoch 721/1000, Train Loss: 0.0815, Train Accuracy: 0.9743, Val Loss: 0.1325, Val Accuracy: 0.9484
Epoch 722/1000, Train Loss: 0.0821, Train Accuracy: 0.9745, Val Loss: 0.1324, Val Accuracy: 0.9484
Epoch 723/1000, Train Loss: 0.0793, Train Accuracy: 0.9746, Val Loss: 0.1323, Val Accuracy: 0.9484
Epoch 724/1000, Train Loss: 0.0817, Train Accuracy: 0.9741, Val Loss: 0.1324, Val Accuracy: 0.9484
Epoch 725/1000, Train Loss: 0.0832, Train Accuracy: 0.9726, Val Loss: 0.1323, Val Accuracy: 0.9484
Epoch 726/1000, Train Loss: 0.0811, Train Accuracy: 0.9736, Val Loss: 0.1321, Val Accuracy: 0.9480
Epoch 727/1000, Train Loss: 0.0814, Train Accuracy: 0.9721, Val Loss: 0.1320, Val Accuracy: 0.9484
Epoch 728/1000, Train Loss: 0.0792, Train Accuracy: 0.9746, Val Loss: 0.1317, Val Accuracy: 0.9480
Epoch 729/1000, Train Loss: 0.0819, Train Accuracy: 0.9738, Val Loss: 0.1315, Val Accuracy: 0.9480
Epoch 730/1000, Train Loss: 0.0849, Train Accuracy: 0.9725, Val Loss: 0.1318, Val Accuracy: 0.9484
Epoch 731/1000, Train Loss: 0.0795, Train Accuracy: 0.9745, Val Loss: 0.1316, Val Accuracy: 0.9480
Epoch 732/1000, Train Loss: 0.0795, Train Accuracy: 0.9736, Val Loss: 0.1313, Val Accuracy: 0.9480
Epoch 733/1000, Train Loss: 0.0788, Train Accuracy: 0.9754, Val Loss: 0.1314, Val Accuracy: 0.9480
Epoch 734/1000, Train Loss: 0.0751, Train Accuracy: 0.9775, Val Loss: 0.1314, Val Accuracy: 0.9480
Epoch 735/1000, Train Loss: 0.0794, Train Accuracy: 0.9749, Val Loss: 0.1313, Val Accuracy: 0.9480
Epoch 736/1000, Train Loss: 0.0813, Train Accuracy: 0.9740, Val Loss: 0.1311, Val Accuracy: 0.9494
Epoch 737/1000, Train Loss: 0.0788, Train Accuracy: 0.9746, Val Loss: 0.1313, Val Accuracy: 0.9480
Epoch 738/1000, Train Loss: 0.0835, Train Accuracy: 0.9741, Val Loss: 0.1313, Val Accuracy: 0.9480
Epoch 739/1000, Train Loss: 0.0805, Train Accuracy: 0.9751, Val Loss: 0.1311, Val Accuracy: 0.9484
Epoch 740/1000, Train Loss: 0.0769, Train Accuracy: 0.9763, Val Loss: 0.1310, Val Accuracy: 0.9484
Epoch 741/1000, Train Loss: 0.0768, Train Accuracy: 0.9760, Val Loss: 0.1309, Val Accuracy: 0.9484
Epoch 742/1000, Train Loss: 0.0779, Train Accuracy: 0.9743, Val Loss: 0.1308, Val Accuracy: 0.9484
Epoch 743/1000, Train Loss: 0.0759, Train Accuracy: 0.9773, Val Loss: 0.1309, Val Accuracy: 0.9475
Epoch 744/1000, Train Loss: 0.0765, Train Accuracy: 0.9771, Val Loss: 0.1308, Val Accuracy: 0.9475
Epoch 745/1000, Train Loss: 0.0780, Train Accuracy: 0.9746, Val Loss: 0.1306, Val Accuracy: 0.9480
Epoch 746/1000, Train Loss: 0.0791, Train Accuracy: 0.9751, Val Loss: 0.1307, Val Accuracy: 0.9475
Epoch 747/1000, Train Loss: 0.0755, Train Accuracy: 0.9769, Val Loss: 0.1308, Val Accuracy: 0.9475
Epoch 748/1000, Train Loss: 0.0754, Train Accuracy: 0.9769, Val Loss: 0.1307, Val Accuracy: 0.9475
Epoch 749/1000, Train Loss: 0.0746, Train Accuracy: 0.9758, Val Loss: 0.1310, Val Accuracy: 0.9484
Epoch 750/1000, Train Loss: 0.0724, Train Accuracy: 0.9782, Val Loss: 0.1309, Val Accuracy: 0.9489
Epoch 751/1000, Train Loss: 0.0801, Train Accuracy: 0.9748, Val Loss: 0.1306, Val Accuracy: 0.9484
Epoch 752/1000, Train Loss: 0.0775, Train Accuracy: 0.9751, Val Loss: 0.1304, Val Accuracy: 0.9489
Epoch 753/1000, Train Loss: 0.0736, Train Accuracy: 0.9777, Val Loss: 0.1304, Val Accuracy: 0.9484
Epoch 754/1000, Train Loss: 0.0734, Train Accuracy: 0.9786, Val Loss: 0.1304, Val Accuracy: 0.9484
Epoch 755/1000, Train Loss: 0.0744, Train Accuracy: 0.9769, Val Loss: 0.1302, Val Accuracy: 0.9484
Epoch 756/1000, Train Loss: 0.0751, Train Accuracy: 0.9765, Val Loss: 0.1303, Val Accuracy: 0.9484
Epoch 757/1000, Train Loss: 0.0755, Train Accuracy: 0.9762, Val Loss: 0.1303, Val Accuracy: 0.9480
Epoch 758/1000, Train Loss: 0.0743, Train Accuracy: 0.9775, Val Loss: 0.1303, Val Accuracy: 0.9484
Epoch 759/1000, Train Loss: 0.0751, Train Accuracy: 0.9757, Val Loss: 0.1300, Val Accuracy: 0.9484
Epoch 760/1000, Train Loss: 0.0735, Train Accuracy: 0.9760, Val Loss: 0.1298, Val Accuracy: 0.9484
Epoch 761/1000, Train Loss: 0.0746, Train Accuracy: 0.9770, Val Loss: 0.1297, Val Accuracy: 0.9475
Epoch 762/1000, Train Loss: 0.0726, Train Accuracy: 0.9777, Val Loss: 0.1297, Val Accuracy: 0.9480
Epoch 763/1000, Train Loss: 0.0734, Train Accuracy: 0.9762, Val Loss: 0.1299, Val Accuracy: 0.9480
Epoch 764/1000, Train Loss: 0.0730, Train Accuracy: 0.9781, Val Loss: 0.1299, Val Accuracy: 0.9480
Epoch 765/1000, Train Loss: 0.0723, Train Accuracy: 0.9775, Val Loss: 0.1299, Val Accuracy: 0.9475
Epoch 766/1000, Train Loss: 0.0703, Train Accuracy: 0.9790, Val Loss: 0.1298, Val Accuracy: 0.9475
Epoch 767/1000, Train Loss: 0.0729, Train Accuracy: 0.9779, Val Loss: 0.1298, Val Accuracy: 0.9480
Epoch 768/1000, Train Loss: 0.0739, Train Accuracy: 0.9766, Val Loss: 0.1295, Val Accuracy: 0.9475
Epoch 769/1000, Train Loss: 0.0710, Train Accuracy: 0.9771, Val Loss: 0.1296, Val Accuracy: 0.9475
Epoch 770/1000, Train Loss: 0.0715, Train Accuracy: 0.9771, Val Loss: 0.1293, Val Accuracy: 0.9489
Epoch 771/1000, Train Loss: 0.0719, Train Accuracy: 0.9766, Val Loss: 0.1296, Val Accuracy: 0.9489
Epoch 772/1000, Train Loss: 0.0722, Train Accuracy: 0.9768, Val Loss: 0.1297, Val Accuracy: 0.9489
Epoch 773/1000, Train Loss: 0.0683, Train Accuracy: 0.9785, Val Loss: 0.1296, Val Accuracy: 0.9489
Epoch 774/1000, Train Loss: 0.0696, Train Accuracy: 0.9803, Val Loss: 0.1295, Val Accuracy: 0.9494
Epoch 775/1000, Train Loss: 0.0741, Train Accuracy: 0.9779, Val Loss: 0.1294, Val Accuracy: 0.9484
Epoch 776/1000, Train Loss: 0.0711, Train Accuracy: 0.9777, Val Loss: 0.1294, Val Accuracy: 0.9484
Epoch 777/1000, Train Loss: 0.0713, Train Accuracy: 0.9790, Val Loss: 0.1295, Val Accuracy: 0.9484
Epoch 778/1000, Train Loss: 0.0702, Train Accuracy: 0.9774, Val Loss: 0.1292, Val Accuracy: 0.9484
Epoch 779/1000, Train Loss: 0.0703, Train Accuracy: 0.9786, Val Loss: 0.1291, Val Accuracy: 0.9484
Epoch 780/1000, Train Loss: 0.0668, Train Accuracy: 0.9803, Val Loss: 0.1290, Val Accuracy: 0.9480
Epoch 781/1000, Train Loss: 0.0696, Train Accuracy: 0.9782, Val Loss: 0.1289, Val Accuracy: 0.9480
Epoch 782/1000, Train Loss: 0.0729, Train Accuracy: 0.9752, Val Loss: 0.1287, Val Accuracy: 0.9480
Epoch 783/1000, Train Loss: 0.0679, Train Accuracy: 0.9783, Val Loss: 0.1286, Val Accuracy: 0.9480
Epoch 784/1000, Train Loss: 0.0706, Train Accuracy: 0.9791, Val Loss: 0.1287, Val Accuracy: 0.9480
Epoch 785/1000, Train Loss: 0.0673, Train Accuracy: 0.9803, Val Loss: 0.1286, Val Accuracy: 0.9475
Epoch 786/1000, Train Loss: 0.0708, Train Accuracy: 0.9773, Val Loss: 0.1287, Val Accuracy: 0.9480
Epoch 787/1000, Train Loss: 0.0707, Train Accuracy: 0.9785, Val Loss: 0.1287, Val Accuracy: 0.9480
Epoch 788/1000, Train Loss: 0.0680, Train Accuracy: 0.9786, Val Loss: 0.1285, Val Accuracy: 0.9480
Epoch 789/1000, Train Loss: 0.0692, Train Accuracy: 0.9765, Val Loss: 0.1285, Val Accuracy: 0.9480
Epoch 790/1000, Train Loss: 0.0683, Train Accuracy: 0.9796, Val Loss: 0.1286, Val Accuracy: 0.9480
Epoch 791/1000, Train Loss: 0.0678, Train Accuracy: 0.9785, Val Loss: 0.1286, Val Accuracy: 0.9480
Epoch 792/1000, Train Loss: 0.0696, Train Accuracy: 0.9776, Val Loss: 0.1283, Val Accuracy: 0.9480
Epoch 793/1000, Train Loss: 0.0640, Train Accuracy: 0.9826, Val Loss: 0.1282, Val Accuracy: 0.9475
Epoch 794/1000, Train Loss: 0.0683, Train Accuracy: 0.9794, Val Loss: 0.1285, Val Accuracy: 0.9489
Epoch 795/1000, Train Loss: 0.0678, Train Accuracy: 0.9788, Val Loss: 0.1283, Val Accuracy: 0.9484
Epoch 796/1000, Train Loss: 0.0686, Train Accuracy: 0.9780, Val Loss: 0.1282, Val Accuracy: 0.9480
Epoch 797/1000, Train Loss: 0.0693, Train Accuracy: 0.9776, Val Loss: 0.1283, Val Accuracy: 0.9484
Epoch 798/1000, Train Loss: 0.0670, Train Accuracy: 0.9796, Val Loss: 0.1284, Val Accuracy: 0.9480
Epoch 799/1000, Train Loss: 0.0658, Train Accuracy: 0.9794, Val Loss: 0.1285, Val Accuracy: 0.9484
Epoch 800/1000, Train Loss: 0.0651, Train Accuracy: 0.9797, Val Loss: 0.1282, Val Accuracy: 0.9484
Epoch 801/1000, Train Loss: 0.0664, Train Accuracy: 0.9805, Val Loss: 0.1284, Val Accuracy: 0.9480
Epoch 802/1000, Train Loss: 0.0659, Train Accuracy: 0.9792, Val Loss: 0.1283, Val Accuracy: 0.9484
Epoch 803/1000, Train Loss: 0.0678, Train Accuracy: 0.9793, Val Loss: 0.1283, Val Accuracy: 0.9489
Epoch 804/1000, Train Loss: 0.0685, Train Accuracy: 0.9794, Val Loss: 0.1279, Val Accuracy: 0.9489
Epoch 805/1000, Train Loss: 0.0658, Train Accuracy: 0.9790, Val Loss: 0.1280, Val Accuracy: 0.9489
Epoch 806/1000, Train Loss: 0.0643, Train Accuracy: 0.9805, Val Loss: 0.1281, Val Accuracy: 0.9489
Epoch 807/1000, Train Loss: 0.0644, Train Accuracy: 0.9796, Val Loss: 0.1280, Val Accuracy: 0.9489
Epoch 808/1000, Train Loss: 0.0641, Train Accuracy: 0.9818, Val Loss: 0.1279, Val Accuracy: 0.9489
Epoch 809/1000, Train Loss: 0.0679, Train Accuracy: 0.9791, Val Loss: 0.1280, Val Accuracy: 0.9484
Epoch 810/1000, Train Loss: 0.0658, Train Accuracy: 0.9819, Val Loss: 0.1280, Val Accuracy: 0.9489
Epoch 811/1000, Train Loss: 0.0647, Train Accuracy: 0.9816, Val Loss: 0.1279, Val Accuracy: 0.9489
Epoch 812/1000, Train Loss: 0.0639, Train Accuracy: 0.9810, Val Loss: 0.1277, Val Accuracy: 0.9484
Epoch 813/1000, Train Loss: 0.0617, Train Accuracy: 0.9827, Val Loss: 0.1277, Val Accuracy: 0.9484
Epoch 814/1000, Train Loss: 0.0680, Train Accuracy: 0.9753, Val Loss: 0.1276, Val Accuracy: 0.9484
Epoch 815/1000, Train Loss: 0.0679, Train Accuracy: 0.9774, Val Loss: 0.1276, Val Accuracy: 0.9489
Epoch 816/1000, Train Loss: 0.0635, Train Accuracy: 0.9803, Val Loss: 0.1275, Val Accuracy: 0.9489
Epoch 817/1000, Train Loss: 0.0649, Train Accuracy: 0.9792, Val Loss: 0.1276, Val Accuracy: 0.9494
Epoch 818/1000, Train Loss: 0.0631, Train Accuracy: 0.9801, Val Loss: 0.1275, Val Accuracy: 0.9489
Epoch 819/1000, Train Loss: 0.0618, Train Accuracy: 0.9816, Val Loss: 0.1277, Val Accuracy: 0.9489
Epoch 820/1000, Train Loss: 0.0630, Train Accuracy: 0.9819, Val Loss: 0.1276, Val Accuracy: 0.9484
Epoch 821/1000, Train Loss: 0.0644, Train Accuracy: 0.9802, Val Loss: 0.1274, Val Accuracy: 0.9475
Epoch 822/1000, Train Loss: 0.0629, Train Accuracy: 0.9808, Val Loss: 0.1276, Val Accuracy: 0.9470
Epoch 823/1000, Train Loss: 0.0600, Train Accuracy: 0.9820, Val Loss: 0.1277, Val Accuracy: 0.9470
Epoch 824/1000, Train Loss: 0.0672, Train Accuracy: 0.9787, Val Loss: 0.1275, Val Accuracy: 0.9470
Epoch 825/1000, Train Loss: 0.0627, Train Accuracy: 0.9828, Val Loss: 0.1273, Val Accuracy: 0.9470
Epoch 826/1000, Train Loss: 0.0602, Train Accuracy: 0.9808, Val Loss: 0.1273, Val Accuracy: 0.9470
Epoch 827/1000, Train Loss: 0.0615, Train Accuracy: 0.9814, Val Loss: 0.1275, Val Accuracy: 0.9470
Epoch 828/1000, Train Loss: 0.0640, Train Accuracy: 0.9810, Val Loss: 0.1274, Val Accuracy: 0.9475
Epoch 829/1000, Train Loss: 0.0621, Train Accuracy: 0.9805, Val Loss: 0.1273, Val Accuracy: 0.9475
Epoch 830/1000, Train Loss: 0.0632, Train Accuracy: 0.9811, Val Loss: 0.1274, Val Accuracy: 0.9475
Epoch 831/1000, Train Loss: 0.0627, Train Accuracy: 0.9813, Val Loss: 0.1276, Val Accuracy: 0.9480
Epoch 832/1000, Train Loss: 0.0654, Train Accuracy: 0.9783, Val Loss: 0.1272, Val Accuracy: 0.9475
Epoch 833/1000, Train Loss: 0.0649, Train Accuracy: 0.9799, Val Loss: 0.1272, Val Accuracy: 0.9465
Epoch 834/1000, Train Loss: 0.0632, Train Accuracy: 0.9792, Val Loss: 0.1270, Val Accuracy: 0.9475
Epoch 835/1000, Train Loss: 0.0609, Train Accuracy: 0.9810, Val Loss: 0.1269, Val Accuracy: 0.9475
Epoch 836/1000, Train Loss: 0.0606, Train Accuracy: 0.9813, Val Loss: 0.1270, Val Accuracy: 0.9475
Epoch 837/1000, Train Loss: 0.0625, Train Accuracy: 0.9807, Val Loss: 0.1269, Val Accuracy: 0.9480
Epoch 838/1000, Train Loss: 0.0594, Train Accuracy: 0.9824, Val Loss: 0.1268, Val Accuracy: 0.9480
Epoch 839/1000, Train Loss: 0.0605, Train Accuracy: 0.9819, Val Loss: 0.1266, Val Accuracy: 0.9480
Epoch 840/1000, Train Loss: 0.0620, Train Accuracy: 0.9818, Val Loss: 0.1265, Val Accuracy: 0.9475
Epoch 841/1000, Train Loss: 0.0607, Train Accuracy: 0.9813, Val Loss: 0.1266, Val Accuracy: 0.9475
Epoch 842/1000, Train Loss: 0.0620, Train Accuracy: 0.9814, Val Loss: 0.1267, Val Accuracy: 0.9480
Epoch 843/1000, Train Loss: 0.0609, Train Accuracy: 0.9822, Val Loss: 0.1266, Val Accuracy: 0.9484
Epoch 844/1000, Train Loss: 0.0597, Train Accuracy: 0.9824, Val Loss: 0.1265, Val Accuracy: 0.9480
Epoch 845/1000, Train Loss: 0.0590, Train Accuracy: 0.9826, Val Loss: 0.1264, Val Accuracy: 0.9484
Epoch 846/1000, Train Loss: 0.0575, Train Accuracy: 0.9838, Val Loss: 0.1265, Val Accuracy: 0.9484
Epoch 847/1000, Train Loss: 0.0583, Train Accuracy: 0.9822, Val Loss: 0.1263, Val Accuracy: 0.9494
Epoch 848/1000, Train Loss: 0.0603, Train Accuracy: 0.9809, Val Loss: 0.1265, Val Accuracy: 0.9484
Epoch 849/1000, Train Loss: 0.0612, Train Accuracy: 0.9808, Val Loss: 0.1267, Val Accuracy: 0.9484
Epoch 850/1000, Train Loss: 0.0585, Train Accuracy: 0.9807, Val Loss: 0.1265, Val Accuracy: 0.9489
Epoch 851/1000, Train Loss: 0.0581, Train Accuracy: 0.9835, Val Loss: 0.1264, Val Accuracy: 0.9489
Epoch 852/1000, Train Loss: 0.0595, Train Accuracy: 0.9808, Val Loss: 0.1266, Val Accuracy: 0.9489
Epoch 853/1000, Train Loss: 0.0578, Train Accuracy: 0.9837, Val Loss: 0.1265, Val Accuracy: 0.9489
Epoch 854/1000, Train Loss: 0.0571, Train Accuracy: 0.9822, Val Loss: 0.1263, Val Accuracy: 0.9494
Epoch 855/1000, Train Loss: 0.0586, Train Accuracy: 0.9832, Val Loss: 0.1262, Val Accuracy: 0.9499
Epoch 856/1000, Train Loss: 0.0578, Train Accuracy: 0.9826, Val Loss: 0.1264, Val Accuracy: 0.9489
Epoch 857/1000, Train Loss: 0.0570, Train Accuracy: 0.9833, Val Loss: 0.1263, Val Accuracy: 0.9494
Epoch 858/1000, Train Loss: 0.0582, Train Accuracy: 0.9828, Val Loss: 0.1264, Val Accuracy: 0.9494
Epoch 859/1000, Train Loss: 0.0582, Train Accuracy: 0.9830, Val Loss: 0.1263, Val Accuracy: 0.9499
Epoch 860/1000, Train Loss: 0.0571, Train Accuracy: 0.9831, Val Loss: 0.1262, Val Accuracy: 0.9499
Epoch 861/1000, Train Loss: 0.0568, Train Accuracy: 0.9846, Val Loss: 0.1261, Val Accuracy: 0.9499
Epoch 862/1000, Train Loss: 0.0569, Train Accuracy: 0.9838, Val Loss: 0.1261, Val Accuracy: 0.9499
Epoch 863/1000, Train Loss: 0.0564, Train Accuracy: 0.9832, Val Loss: 0.1261, Val Accuracy: 0.9499
Epoch 864/1000, Train Loss: 0.0576, Train Accuracy: 0.9837, Val Loss: 0.1265, Val Accuracy: 0.9489
Epoch 865/1000, Train Loss: 0.0567, Train Accuracy: 0.9839, Val Loss: 0.1267, Val Accuracy: 0.9484
Epoch 866/1000, Train Loss: 0.0545, Train Accuracy: 0.9838, Val Loss: 0.1264, Val Accuracy: 0.9489
Epoch 867/1000, Train Loss: 0.0580, Train Accuracy: 0.9821, Val Loss: 0.1262, Val Accuracy: 0.9489
Epoch 868/1000, Train Loss: 0.0550, Train Accuracy: 0.9842, Val Loss: 0.1264, Val Accuracy: 0.9484
Epoch 869/1000, Train Loss: 0.0555, Train Accuracy: 0.9827, Val Loss: 0.1264, Val Accuracy: 0.9489
Epoch 870/1000, Train Loss: 0.0550, Train Accuracy: 0.9831, Val Loss: 0.1261, Val Accuracy: 0.9499
Epoch 871/1000, Train Loss: 0.0568, Train Accuracy: 0.9832, Val Loss: 0.1260, Val Accuracy: 0.9494
Epoch 872/1000, Train Loss: 0.0563, Train Accuracy: 0.9848, Val Loss: 0.1262, Val Accuracy: 0.9499
Epoch 873/1000, Train Loss: 0.0544, Train Accuracy: 0.9856, Val Loss: 0.1262, Val Accuracy: 0.9499
Epoch 874/1000, Train Loss: 0.0556, Train Accuracy: 0.9833, Val Loss: 0.1262, Val Accuracy: 0.9494
Epoch 875/1000, Train Loss: 0.0565, Train Accuracy: 0.9831, Val Loss: 0.1261, Val Accuracy: 0.9494
Epoch 876/1000, Train Loss: 0.0548, Train Accuracy: 0.9849, Val Loss: 0.1261, Val Accuracy: 0.9494
Epoch 877/1000, Train Loss: 0.0575, Train Accuracy: 0.9827, Val Loss: 0.1260, Val Accuracy: 0.9489
Epoch 878/1000, Train Loss: 0.0539, Train Accuracy: 0.9836, Val Loss: 0.1259, Val Accuracy: 0.9494
Epoch 879/1000, Train Loss: 0.0569, Train Accuracy: 0.9825, Val Loss: 0.1260, Val Accuracy: 0.9499
Epoch 880/1000, Train Loss: 0.0539, Train Accuracy: 0.9860, Val Loss: 0.1261, Val Accuracy: 0.9499
Epoch 881/1000, Train Loss: 0.0539, Train Accuracy: 0.9838, Val Loss: 0.1262, Val Accuracy: 0.9494
Epoch 882/1000, Train Loss: 0.0543, Train Accuracy: 0.9841, Val Loss: 0.1261, Val Accuracy: 0.9499
Epoch 883/1000, Train Loss: 0.0544, Train Accuracy: 0.9850, Val Loss: 0.1260, Val Accuracy: 0.9499
Epoch 884/1000, Train Loss: 0.0535, Train Accuracy: 0.9854, Val Loss: 0.1260, Val Accuracy: 0.9504
Epoch 885/1000, Train Loss: 0.0532, Train Accuracy: 0.9856, Val Loss: 0.1260, Val Accuracy: 0.9504
Epoch 886/1000, Train Loss: 0.0553, Train Accuracy: 0.9835, Val Loss: 0.1260, Val Accuracy: 0.9494
Epoch 887/1000, Train Loss: 0.0509, Train Accuracy: 0.9853, Val Loss: 0.1258, Val Accuracy: 0.9504
Epoch 888/1000, Train Loss: 0.0531, Train Accuracy: 0.9844, Val Loss: 0.1256, Val Accuracy: 0.9504
Epoch 889/1000, Train Loss: 0.0535, Train Accuracy: 0.9848, Val Loss: 0.1256, Val Accuracy: 0.9499
Epoch 890/1000, Train Loss: 0.0540, Train Accuracy: 0.9843, Val Loss: 0.1255, Val Accuracy: 0.9504
Epoch 891/1000, Train Loss: 0.0527, Train Accuracy: 0.9855, Val Loss: 0.1255, Val Accuracy: 0.9509
Epoch 892/1000, Train Loss: 0.0522, Train Accuracy: 0.9855, Val Loss: 0.1254, Val Accuracy: 0.9509
Epoch 893/1000, Train Loss: 0.0554, Train Accuracy: 0.9841, Val Loss: 0.1255, Val Accuracy: 0.9504
Epoch 894/1000, Train Loss: 0.0526, Train Accuracy: 0.9846, Val Loss: 0.1256, Val Accuracy: 0.9504
Epoch 895/1000, Train Loss: 0.0523, Train Accuracy: 0.9852, Val Loss: 0.1257, Val Accuracy: 0.9504
Epoch 896/1000, Train Loss: 0.0531, Train Accuracy: 0.9839, Val Loss: 0.1255, Val Accuracy: 0.9499
Epoch 897/1000, Train Loss: 0.0538, Train Accuracy: 0.9849, Val Loss: 0.1256, Val Accuracy: 0.9504
Epoch 898/1000, Train Loss: 0.0542, Train Accuracy: 0.9837, Val Loss: 0.1258, Val Accuracy: 0.9504
Epoch 899/1000, Train Loss: 0.0512, Train Accuracy: 0.9859, Val Loss: 0.1260, Val Accuracy: 0.9499
Epoch 900/1000, Train Loss: 0.0510, Train Accuracy: 0.9847, Val Loss: 0.1255, Val Accuracy: 0.9509
Epoch 901/1000, Train Loss: 0.0513, Train Accuracy: 0.9852, Val Loss: 0.1254, Val Accuracy: 0.9514
Epoch 902/1000, Train Loss: 0.0508, Train Accuracy: 0.9855, Val Loss: 0.1254, Val Accuracy: 0.9514
Epoch 903/1000, Train Loss: 0.0521, Train Accuracy: 0.9852, Val Loss: 0.1253, Val Accuracy: 0.9514
Epoch 904/1000, Train Loss: 0.0489, Train Accuracy: 0.9882, Val Loss: 0.1252, Val Accuracy: 0.9514
Epoch 905/1000, Train Loss: 0.0493, Train Accuracy: 0.9867, Val Loss: 0.1252, Val Accuracy: 0.9509
Epoch 906/1000, Train Loss: 0.0518, Train Accuracy: 0.9848, Val Loss: 0.1253, Val Accuracy: 0.9509
Epoch 907/1000, Train Loss: 0.0512, Train Accuracy: 0.9852, Val Loss: 0.1251, Val Accuracy: 0.9509
Epoch 908/1000, Train Loss: 0.0516, Train Accuracy: 0.9858, Val Loss: 0.1251, Val Accuracy: 0.9509
Epoch 909/1000, Train Loss: 0.0535, Train Accuracy: 0.9843, Val Loss: 0.1251, Val Accuracy: 0.9504
Epoch 910/1000, Train Loss: 0.0514, Train Accuracy: 0.9844, Val Loss: 0.1252, Val Accuracy: 0.9509
Epoch 911/1000, Train Loss: 0.0499, Train Accuracy: 0.9863, Val Loss: 0.1249, Val Accuracy: 0.9509
Epoch 912/1000, Train Loss: 0.0513, Train Accuracy: 0.9849, Val Loss: 0.1249, Val Accuracy: 0.9504
Epoch 913/1000, Train Loss: 0.0512, Train Accuracy: 0.9842, Val Loss: 0.1251, Val Accuracy: 0.9514
Epoch 914/1000, Train Loss: 0.0497, Train Accuracy: 0.9849, Val Loss: 0.1250, Val Accuracy: 0.9514
Epoch 915/1000, Train Loss: 0.0505, Train Accuracy: 0.9855, Val Loss: 0.1250, Val Accuracy: 0.9518
Epoch 916/1000, Train Loss: 0.0508, Train Accuracy: 0.9855, Val Loss: 0.1249, Val Accuracy: 0.9509
Epoch 917/1000, Train Loss: 0.0519, Train Accuracy: 0.9858, Val Loss: 0.1249, Val Accuracy: 0.9514
Epoch 918/1000, Train Loss: 0.0510, Train Accuracy: 0.9859, Val Loss: 0.1250, Val Accuracy: 0.9514
Epoch 919/1000, Train Loss: 0.0495, Train Accuracy: 0.9852, Val Loss: 0.1251, Val Accuracy: 0.9509
Epoch 920/1000, Train Loss: 0.0514, Train Accuracy: 0.9837, Val Loss: 0.1248, Val Accuracy: 0.9514
Epoch 921/1000, Train Loss: 0.0498, Train Accuracy: 0.9856, Val Loss: 0.1250, Val Accuracy: 0.9514
Epoch 922/1000, Train Loss: 0.0485, Train Accuracy: 0.9870, Val Loss: 0.1250, Val Accuracy: 0.9509
Epoch 923/1000, Train Loss: 0.0507, Train Accuracy: 0.9842, Val Loss: 0.1249, Val Accuracy: 0.9518
Epoch 924/1000, Train Loss: 0.0488, Train Accuracy: 0.9856, Val Loss: 0.1250, Val Accuracy: 0.9514
Epoch 925/1000, Train Loss: 0.0484, Train Accuracy: 0.9858, Val Loss: 0.1250, Val Accuracy: 0.9504
Epoch 926/1000, Train Loss: 0.0480, Train Accuracy: 0.9881, Val Loss: 0.1251, Val Accuracy: 0.9509
Epoch 927/1000, Train Loss: 0.0481, Train Accuracy: 0.9880, Val Loss: 0.1250, Val Accuracy: 0.9514
Epoch 928/1000, Train Loss: 0.0468, Train Accuracy: 0.9873, Val Loss: 0.1250, Val Accuracy: 0.9514
Epoch 929/1000, Train Loss: 0.0500, Train Accuracy: 0.9853, Val Loss: 0.1251, Val Accuracy: 0.9514
Epoch 930/1000, Train Loss: 0.0504, Train Accuracy: 0.9856, Val Loss: 0.1250, Val Accuracy: 0.9509
Epoch 931/1000, Train Loss: 0.0476, Train Accuracy: 0.9875, Val Loss: 0.1253, Val Accuracy: 0.9509
Epoch 932/1000, Train Loss: 0.0475, Train Accuracy: 0.9878, Val Loss: 0.1252, Val Accuracy: 0.9509
Epoch 933/1000, Train Loss: 0.0458, Train Accuracy: 0.9883, Val Loss: 0.1253, Val Accuracy: 0.9514
Epoch 934/1000, Train Loss: 0.0460, Train Accuracy: 0.9876, Val Loss: 0.1252, Val Accuracy: 0.9514
Epoch 935/1000, Train Loss: 0.0478, Train Accuracy: 0.9864, Val Loss: 0.1253, Val Accuracy: 0.9509
Epoch 936/1000, Train Loss: 0.0464, Train Accuracy: 0.9866, Val Loss: 0.1252, Val Accuracy: 0.9509
Epoch 937/1000, Train Loss: 0.0481, Train Accuracy: 0.9855, Val Loss: 0.1253, Val Accuracy: 0.9509
Epoch 938/1000, Train Loss: 0.0443, Train Accuracy: 0.9883, Val Loss: 0.1252, Val Accuracy: 0.9514
Epoch 939/1000, Train Loss: 0.0477, Train Accuracy: 0.9867, Val Loss: 0.1250, Val Accuracy: 0.9514
Epoch 940/1000, Train Loss: 0.0492, Train Accuracy: 0.9863, Val Loss: 0.1248, Val Accuracy: 0.9523
Epoch 941/1000, Train Loss: 0.0452, Train Accuracy: 0.9888, Val Loss: 0.1247, Val Accuracy: 0.9523
Epoch 942/1000, Train Loss: 0.0469, Train Accuracy: 0.9870, Val Loss: 0.1248, Val Accuracy: 0.9518
Epoch 943/1000, Train Loss: 0.0469, Train Accuracy: 0.9856, Val Loss: 0.1248, Val Accuracy: 0.9518
Epoch 944/1000, Train Loss: 0.0469, Train Accuracy: 0.9882, Val Loss: 0.1248, Val Accuracy: 0.9518
Epoch 945/1000, Train Loss: 0.0447, Train Accuracy: 0.9877, Val Loss: 0.1249, Val Accuracy: 0.9518
Epoch 946/1000, Train Loss: 0.0494, Train Accuracy: 0.9853, Val Loss: 0.1248, Val Accuracy: 0.9518
Epoch 947/1000, Train Loss: 0.0482, Train Accuracy: 0.9853, Val Loss: 0.1246, Val Accuracy: 0.9523
Epoch 948/1000, Train Loss: 0.0442, Train Accuracy: 0.9892, Val Loss: 0.1246, Val Accuracy: 0.9523
Epoch 949/1000, Train Loss: 0.0447, Train Accuracy: 0.9878, Val Loss: 0.1248, Val Accuracy: 0.9523
Epoch 950/1000, Train Loss: 0.0460, Train Accuracy: 0.9876, Val Loss: 0.1249, Val Accuracy: 0.9528
Epoch 951/1000, Train Loss: 0.0454, Train Accuracy: 0.9880, Val Loss: 0.1248, Val Accuracy: 0.9528
Epoch 952/1000, Train Loss: 0.0472, Train Accuracy: 0.9875, Val Loss: 0.1249, Val Accuracy: 0.9523
Epoch 953/1000, Train Loss: 0.0464, Train Accuracy: 0.9869, Val Loss: 0.1252, Val Accuracy: 0.9518
Epoch 954/1000, Train Loss: 0.0481, Train Accuracy: 0.9865, Val Loss: 0.1250, Val Accuracy: 0.9528
Epoch 955/1000, Train Loss: 0.0469, Train Accuracy: 0.9866, Val Loss: 0.1251, Val Accuracy: 0.9523
Epoch 956/1000, Train Loss: 0.0427, Train Accuracy: 0.9891, Val Loss: 0.1251, Val Accuracy: 0.9528
Epoch 957/1000, Train Loss: 0.0451, Train Accuracy: 0.9881, Val Loss: 0.1251, Val Accuracy: 0.9518
Epoch 958/1000, Train Loss: 0.0444, Train Accuracy: 0.9884, Val Loss: 0.1254, Val Accuracy: 0.9509
Epoch 959/1000, Train Loss: 0.0453, Train Accuracy: 0.9887, Val Loss: 0.1253, Val Accuracy: 0.9509
Epoch 960/1000, Train Loss: 0.0465, Train Accuracy: 0.9858, Val Loss: 0.1254, Val Accuracy: 0.9514
Epoch 961/1000, Train Loss: 0.0446, Train Accuracy: 0.9875, Val Loss: 0.1253, Val Accuracy: 0.9518
Epoch 962/1000, Train Loss: 0.0422, Train Accuracy: 0.9881, Val Loss: 0.1252, Val Accuracy: 0.9523
Epoch 963/1000, Train Loss: 0.0444, Train Accuracy: 0.9876, Val Loss: 0.1252, Val Accuracy: 0.9523
Epoch 964/1000, Train Loss: 0.0423, Train Accuracy: 0.9884, Val Loss: 0.1252, Val Accuracy: 0.9523
Epoch 965/1000, Train Loss: 0.0423, Train Accuracy: 0.9881, Val Loss: 0.1250, Val Accuracy: 0.9523
Epoch 966/1000, Train Loss: 0.0437, Train Accuracy: 0.9878, Val Loss: 0.1252, Val Accuracy: 0.9518
Epoch 967/1000, Train Loss: 0.0446, Train Accuracy: 0.9880, Val Loss: 0.1254, Val Accuracy: 0.9518
Epoch 968/1000, Train Loss: 0.0437, Train Accuracy: 0.9866, Val Loss: 0.1255, Val Accuracy: 0.9518
Epoch 969/1000, Train Loss: 0.0441, Train Accuracy: 0.9882, Val Loss: 0.1255, Val Accuracy: 0.9514
Epoch 970/1000, Train Loss: 0.0433, Train Accuracy: 0.9878, Val Loss: 0.1256, Val Accuracy: 0.9514
Epoch 971/1000, Train Loss: 0.0445, Train Accuracy: 0.9863, Val Loss: 0.1256, Val Accuracy: 0.9514
Epoch 972/1000, Train Loss: 0.0420, Train Accuracy: 0.9889, Val Loss: 0.1253, Val Accuracy: 0.9514
Epoch 973/1000, Train Loss: 0.0430, Train Accuracy: 0.9881, Val Loss: 0.1253, Val Accuracy: 0.9514
Epoch 974/1000, Train Loss: 0.0437, Train Accuracy: 0.9881, Val Loss: 0.1252, Val Accuracy: 0.9518
Epoch 975/1000, Train Loss: 0.0430, Train Accuracy: 0.9891, Val Loss: 0.1251, Val Accuracy: 0.9514
Epoch 976/1000, Train Loss: 0.0426, Train Accuracy: 0.9891, Val Loss: 0.1252, Val Accuracy: 0.9514
Epoch 977/1000, Train Loss: 0.0418, Train Accuracy: 0.9884, Val Loss: 0.1253, Val Accuracy: 0.9514
Epoch 978/1000, Train Loss: 0.0416, Train Accuracy: 0.9886, Val Loss: 0.1252, Val Accuracy: 0.9514
Epoch 979/1000, Train Loss: 0.0419, Train Accuracy: 0.9877, Val Loss: 0.1254, Val Accuracy: 0.9514
Epoch 980/1000, Train Loss: 0.0458, Train Accuracy: 0.9865, Val Loss: 0.1253, Val Accuracy: 0.9514
Epoch 981/1000, Train Loss: 0.0424, Train Accuracy: 0.9898, Val Loss: 0.1252, Val Accuracy: 0.9514
Epoch 982/1000, Train Loss: 0.0421, Train Accuracy: 0.9882, Val Loss: 0.1252, Val Accuracy: 0.9514
Epoch 983/1000, Train Loss: 0.0432, Train Accuracy: 0.9870, Val Loss: 0.1253, Val Accuracy: 0.9518
Epoch 984/1000, Train Loss: 0.0432, Train Accuracy: 0.9869, Val Loss: 0.1252, Val Accuracy: 0.9514
Epoch 985/1000, Train Loss: 0.0403, Train Accuracy: 0.9889, Val Loss: 0.1252, Val Accuracy: 0.9514
Epoch 986/1000, Train Loss: 0.0409, Train Accuracy: 0.9878, Val Loss: 0.1254, Val Accuracy: 0.9514
Epoch 987/1000, Train Loss: 0.0394, Train Accuracy: 0.9908, Val Loss: 0.1254, Val Accuracy: 0.9514
Epoch 988/1000, Train Loss: 0.0405, Train Accuracy: 0.9895, Val Loss: 0.1254, Val Accuracy: 0.9514
Epoch 989/1000, Train Loss: 0.0420, Train Accuracy: 0.9892, Val Loss: 0.1251, Val Accuracy: 0.9518
Epoch 990/1000, Train Loss: 0.0427, Train Accuracy: 0.9882, Val Loss: 0.1252, Val Accuracy: 0.9518
Epoch 991/1000, Train Loss: 0.0419, Train Accuracy: 0.9887, Val Loss: 0.1248, Val Accuracy: 0.9518
Epoch 992/1000, Train Loss: 0.0404, Train Accuracy: 0.9887, Val Loss: 0.1249, Val Accuracy: 0.9523
Epoch 993/1000, Train Loss: 0.0391, Train Accuracy: 0.9906, Val Loss: 0.1249, Val Accuracy: 0.9523
Epoch 994/1000, Train Loss: 0.0410, Train Accuracy: 0.9891, Val Loss: 0.1252, Val Accuracy: 0.9523
Epoch 995/1000, Train Loss: 0.0396, Train Accuracy: 0.9897, Val Loss: 0.1251, Val Accuracy: 0.9523
Epoch 996/1000, Train Loss: 0.0417, Train Accuracy: 0.9884, Val Loss: 0.1254, Val Accuracy: 0.9523
Epoch 997/1000, Train Loss: 0.0394, Train Accuracy: 0.9901, Val Loss: 0.1254, Val Accuracy: 0.9514
Epoch 998/1000, Train Loss: 0.0389, Train Accuracy: 0.9883, Val Loss: 0.1254, Val Accuracy: 0.9514
Epoch 999/1000, Train Loss: 0.0403, Train Accuracy: 0.9897, Val Loss: 0.1255, Val Accuracy: 0.9514
Epoch 1000/1000, Train Loss: 0.0395, Train Accuracy: 0.9888, Val Loss: 0.1253, Val Accuracy: 0.9518
In [ ]:
# Create the neural network classifier
clf = MLPClassifier(random_state=42)

# Assuming you have performed a grid search and obtained the best estimator
# Define the parameter grid
param_grid = {
    'hidden_layer_sizes': [(30,30), (10,10,10)],
    'solver': ['adam', 'sgd'],
    'alpha': [0.001, 0.01],
}

# Create the GridSearchCV object
grid_search = GridSearchCV(clf, param_grid, scoring='accuracy', cv=2)

# Fit the GridSearchCV object to the data
grid_search.fit(X_train_DS_HCC1806_nn, y_train_DS_HCC1806_nn)


# Save the best estimator in best_nn
best_nn_DS_HCC1806 = grid_search.best_estimator_
print(best_nn_DS_HCC1806)

c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(

MLPClassifier(alpha=0.01, hidden_layer_sizes=(30, 30), random_state=42)
In [ ]:
torch.save(model_DS_H.state_dict(), best_model_path_H)
¶

Test for the model drop-seq HCC1806

This below is the step for testing our neural network with HCC1806 dataset.

As before the model is set to evaluation mode, and test data is converted to PyTorch tensors.

A forward pass is performed on the test data to obtain predictions. The test loss is calculated using the same loss function as before and predictions are converted to binary values by applying a sigmoid function followed by rounding. Finally, The number of correct predictions is counted and used to compute the test accuracy.

In [ ]:
# Load the best model
model_DS_H.load_state_dict(torch.load('best_model_final_H.pth'))

# Evaluate on test data
model_DS_H.eval()
X_test_tensor_H = torch.tensor(X_test_df_DS_HCC1806.values, dtype=torch.float32)
y_test_tensor_H = torch.tensor(y_test_df_DS_HCC1806.values, dtype=torch.float32).unsqueeze(1)


with torch.no_grad():
    test_output = model_DS_H(X_test_tensor_H)
    test_loss = criterion(test_output, y_test_tensor_H)
    test_predictions = torch.round(torch.sigmoid(test_output))
    test_correct = (test_predictions == y_test_tensor_H).sum().item()
    test_total = y_test_tensor_H.size(0)
    test_acc = test_correct / test_total

print(f'Test Loss: {test_loss.item():.4f}, Test Accuracy: {test_acc:.4f}')

Test Loss: 0.1195, Test Accuracy: 0.9544

¶

4.4.5) KNN

In [ ]:
best_knn_DS_HCC1806=train_and_evaluate_knn(X_train_DS_HCC, X_test_DS_HCC, y_train_DS_HCC, y_test_DS_HCC)

Best hyperparameters:  {'n_neighbors': 3}
Accuracy: 0.47020769492679604
F1: 0.30035971223021585
Classification Report:
              precision    recall  f1-score   support

         0.0       0.42      0.89      0.57      1171
         1.0       0.73      0.19      0.30      1766

    accuracy                           0.47      2937
   macro avg       0.58      0.54      0.44      2937
weighted avg       0.61      0.47      0.41      2937

¶

4.4.6) Logistic Regression

In [ ]:
best_logreg_DS_HCC1806=train_and_evaluate_logreg(X_train_DS_HCC, X_test_DS_HCC, y_train_DS_HCC, y_test_DS_HCC)

Best hyperparameters:  {'C': 0.002, 'penalty': 'l2'}
Accuracy: 0.9482465100442629
F1: 0.9565962307252998
Classification Report:
              precision    recall  f1-score   support

         0.0       0.92      0.95      0.94      1171
         1.0       0.96      0.95      0.96      1766

    accuracy                           0.95      2937
   macro avg       0.94      0.95      0.95      2937
weighted avg       0.95      0.95      0.95      2937
In [ ]:
# try pca method to reduce number of features and use log log regression
In [ ]:
X_train_pca_DS_HCC, X_test_pca_DS_HCC, y_train_DS_HCC, y_test_DS_HCC,scaler_DS_HCC,pca_DS_HCC = prepare_data_pca(df_trans_DS_HCC)
In [ ]:
X_train_pca_DS_HCC.shape
Out[ ]:
(11745, 1760)
In [ ]:
model_logreg_hcc_DROP_ver2_DS_HCC=train_and_evaluate_logreg(X_train_pca_DS_HCC, X_test_pca_DS_HCC, y_train_DS_HCC, y_test_DS_HCC)

Best hyperparameters:  {'C': 0.002, 'penalty': 'l2'}
Accuracy: 0.9472250595846101
F1: 0.955953395851094
Classification Report:
              precision    recall  f1-score   support

         0.0       0.93      0.94      0.93      1171
         1.0       0.96      0.95      0.96      1766

    accuracy                           0.95      2937
   macro avg       0.94      0.95      0.95      2937
weighted avg       0.95      0.95      0.95      2937

¶

4.4.7) CatBoost

In [ ]:
best_catboost_DS_HCC1806=train_and_evaluate_catboost(X_train_DS_HCC, X_test_DS_HCC, y_train_DS_HCC, y_test_DS_HCC)

Best hyperparameters:  {'logging_level': 'Silent'}
Accuracy: 0.9564181137214846
F1: 0.9637188208616779
Classification Report:
              precision    recall  f1-score   support

         0.0       0.94      0.95      0.95      1171
         1.0       0.96      0.96      0.96      1766

    accuracy                           0.96      2937
   macro avg       0.95      0.95      0.95      2937
weighted avg       0.96      0.96      0.96      2937
In [ ]:
new_columns_names_DS_HCC = []
for i in range(1,3001):
    name_new_DS_HCC = f'f{i}'
    new_columns_names_DS_HCC.append(name_new_DS_HCC)
X_test_pd_DS_HCC=pd.DataFrame(X_test_DS_HCC, columns=new_columns_names_DS_HCC)
X_train_pd_DS_HCC=pd.DataFrame(X_train_DS_HCC, columns=new_columns_names_DS_HCC)
In [ ]:
model_catboost_hcc_DROP_ver2_DS_HCC,cols_cut_DS_HCC=train_and_evaluate_catboost2(X_train_pd_DS_HCC, X_test_pd_DS_HCC, y_train_DS_HCC, y_test_DS_HCC)

229
Best hyperparameters:  {'l2_leaf_reg': 6, 'logging_level': 'Silent', 'n_estimators': 1500}
Accuracy: 0.9605039155600953
F1: 0.966989186112692
Classification Report:
              precision    recall  f1-score   support

         0.0       0.94      0.96      0.95      1171
         1.0       0.97      0.96      0.97      1766

    accuracy                           0.96      2937
   macro avg       0.96      0.96      0.96      2937
weighted avg       0.96      0.96      0.96      2937

¶

4.5) PREDICTORS ON DROPSEQ MERGED (Neural Network)

In [ ]:
# Merge the two DataFrames with suffixes for overlapping columns
DS_merged = df_DS_MCF7_norm_filtT.T.join(df_DS_HCC1806_norm_filtT.T, how="outer", lsuffix='_MCF7', rsuffix='_HCC1806')
DS_merged_complete = DS_merged.fillna(0)    # Replace NaN values with 0
DS_merged_complete
Out[ ]:
"AAAAACCTATCG_Normoxia" "AAAACAACCCTA_Normoxia" "AAAACACTCTCA_Normoxia" "AAAACCAGGCAC_Normoxia" "AAAACCTAGCTC_Normoxia" "AAAACCTCCGGG_Normoxia" "AAAACTCGTTGC_Normoxia" "AAAAGAGCTCTC_Normoxia" "AAAAGCTAGGCG_Normoxia" "AAAATCGCATTT_Normoxia" ... "TTTTCTGATGGT_Hypoxia" "TTTTGATTCAGA_Hypoxia" "TTTTGCAACTGA_Hypoxia" "TTTTGCCGGGCC_Hypoxia" "TTTTGTTAGCCT_Hypoxia" "TTTTTACCAATC_Hypoxia" "TTTTTCCGTGCA_Hypoxia" "TTTTTGCCTGGG_Hypoxia" "TTTTTGTAACAG_Hypoxia" "TTTTTTTGAATC_Hypoxia"
"A4GALT" 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
"AAAS" 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
"AAGAB" 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
"AAMDC" 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
"AAMP" 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
"ZYG11A" 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
"ZYX" 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
"ZZEF1" 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
"ZZZ3" 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
target 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

5167 rows × 36308 columns

We now define the neural network model to be used with the merged datasets dropseq MCF7 and HCC1806

In [ ]:
model_merged_DS = NeuralNetworkDropSeqMerged()
model_merged_DS
Out[ ]:
NeuralNetworkDropSeqMerged(
  (model): Sequential(
    (0): Flatten(start_dim=1, end_dim=-1)
    (1): Linear(in_features=5166, out_features=512, bias=True)
    (2): LeakyReLU(negative_slope=0.01)
    (3): Dropout(p=0.4, inplace=False)
    (4): Linear(in_features=512, out_features=256, bias=True)
    (5): LeakyReLU(negative_slope=0.01)
    (6): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (7): Dropout(p=0.4, inplace=False)
    (8): Linear(in_features=256, out_features=128, bias=True)
    (9): LeakyReLU(negative_slope=0.01)
    (10): Dropout(p=0.3, inplace=False)
    (11): Linear(in_features=128, out_features=64, bias=True)
    (12): LeakyReLU(negative_slope=0.01)
    (13): Linear(in_features=64, out_features=1, bias=True)
  )
)
In [ ]:
DS_merged_complete_T = DS_merged_complete.T
DS_merged_complete_T.shape
Out[ ]:
(36308, 5167)

We split the dataset and labels into train/test dataset and labels, and then we split again the train dataset into train one and the validation one. Then we convert these variables into tensors in order to be able to use them with pytorch.

In [ ]:
# Exclude the column using its name

# Exclude the column using its name
X_merged = DS_merged_complete_T.iloc[:, :-1].values
y_merged = DS_merged_complete_T.iloc[:, -1].values    

# Train/Test Split (starting with 80% train, 20% test)
X_train_merged, X_test_merged, y_train_merged, y_test_merged = train_test_split(X_merged, y_merged, test_size=0.2, random_state=42)

# We do an addition splitting of the training set into the training set and a validation set (useful to tune the hyperparameters)
X_train_merged_nn, X_val_merged_nn, y_train_merged_nn, y_val_merged_nn = train_test_split(X_train_merged, y_train_merged, test_size=0.2, random_state=42)
In [ ]:
# Transform them into tensors

X_train_tensor_merged = torch.tensor(X_train_merged_nn, dtype=torch.float32)
y_train_tensor_merged = torch.tensor(y_train_merged_nn, dtype=torch.float32).unsqueeze(1)  # For binary classification
X_val_tensor_merged = torch.tensor(X_val_merged_nn, dtype=torch.float32)
y_val_tensor_merged = torch.tensor(y_val_merged_nn, dtype=torch.float32).unsqueeze(1)  # For binary classification
X_val_tensor_merged
Out[ ]:
tensor([[0., 0., 0.,  ..., 0., 0., 0.],
        [0., 0., 0.,  ..., 0., 0., 0.],
        [0., 0., 0.,  ..., 0., 0., 0.],
        ...,
        [0., 0., 0.,  ..., 0., 0., 0.],
        [0., 0., 0.,  ..., 0., 0., 0.],
        [0., 0., 0.,  ..., 0., 0., 0.]])
¶

Training for the model drop-seq merged

Here below there is the training process of the neural network using the merged dataset.

It follows the same structure of the training process used with the individual MCF7 and HCC1806 datasets; therefore we use Stochastic Gradient Descent (SGD) as the optimizer with a learning rate of 0.01, and asa loss function the binary cross-entropy with logits ('BCEWithLogitsLoss'). Also here, the training runs for 1000 epochs.

The training loop starts by setting the model to training mode, resetting the gradients, performing a forward pass on the training data, calculating the training loss, and then using backpropagation to compute the gradients. The optimizer updates the model parameters based on these gradients. Finally, training accuracy is calculated by rounding the sigmoid-activated predictions and comparing them to the true labels.

For validation, the model is set to evaluation mode, and a forward pass is performed on the validation data. The validation loss and accuracy are calculated similarly to the training process but without computing gradients.

In [ ]:
optimizer = torch.optim.SGD(model_merged_DS.parameters(), lr=0.01)
criterion = nn.BCEWithLogitsLoss() # applies a sigmoid activation to the model's raw logits and calculates the binary cross-entropy loss

epochs = 1000
best_val_acc = 0
patience = 3
no_improve_epochs = 0

best_model_path_merged = 'best_model_final_merged.pth'

for epoch in range(epochs):
    model_merged_DS.train()
    optimizer.zero_grad() # resets the gradients of all model parameters to zero.
    train_output = model_merged_DS(X_train_tensor_merged)  # Forward pass with all training data, computes the model's predictions (output) for the entire training dataset
    train_loss = criterion(train_output, y_train_tensor_merged) # calculates the difference between predicted outputs and actual labels
    train_loss.backward() #computes the gradients of the loss with respect to the model's parameters
    # This uses backpropagation to distribute the error back through the network
    optimizer.step() #The optimizer applies the selected algorithm (like SGD) to update the weights
    
    # Calculate training accuracy
    with torch.no_grad():
        train_predictions = torch.round(torch.sigmoid(train_output))
        train_correct = (train_predictions == y_train_tensor_merged).sum().item()
        train_total = y_train_tensor_merged.size(0)
        train_acc = train_correct / train_total

    # Validation
    model_merged_DS.eval() #  sets the model to evaluation mode, disabling features like dropout and batch normalization to ensure consistency
    with torch.no_grad(): #prevents PyTorch from computing gradients during evaluation.
        val_output = model_merged_DS(X_val_tensor_merged) #computes predictions on the validation dataset
        val_loss = criterion(val_output, y_val_tensor_merged)
        val_predictions = torch.round(torch.sigmoid(val_output))  # converts the logits/predictions into binary predictions (0 or 1)
        val_correct = (val_predictions == y_val_tensor_merged).sum().item()
        val_total = y_val_tensor_merged.size(0)
        val_acc = val_correct / val_total

    print(f'Epoch {epoch+1}/{epochs}, '
          f'Train Loss: {train_loss.item():.4f}, '
          f'Train Accuracy: {train_acc:.4f}, '
          f'Val Loss: {val_loss.item():.4f}, '
          f'Val Accuracy: {val_acc:.4f}')
    
    

# Save model after all epochs are completed
#torch.save(model.state_dict(), best_model_path) 

Epoch 1/1000, Train Loss: 0.6990, Train Accuracy: 0.4920, Val Loss: 0.6973, Val Accuracy: 0.4873
Epoch 2/1000, Train Loss: 0.6982, Train Accuracy: 0.4918, Val Loss: 0.6972, Val Accuracy: 0.4873
Epoch 3/1000, Train Loss: 0.6975, Train Accuracy: 0.4930, Val Loss: 0.6972, Val Accuracy: 0.4873
Epoch 4/1000, Train Loss: 0.6971, Train Accuracy: 0.4932, Val Loss: 0.6971, Val Accuracy: 0.4873
Epoch 5/1000, Train Loss: 0.6959, Train Accuracy: 0.4947, Val Loss: 0.6970, Val Accuracy: 0.4873
Epoch 6/1000, Train Loss: 0.6951, Train Accuracy: 0.4940, Val Loss: 0.6969, Val Accuracy: 0.4873
Epoch 7/1000, Train Loss: 0.6939, Train Accuracy: 0.4967, Val Loss: 0.6968, Val Accuracy: 0.4873
Epoch 8/1000, Train Loss: 0.6935, Train Accuracy: 0.4958, Val Loss: 0.6968, Val Accuracy: 0.4873
Epoch 9/1000, Train Loss: 0.6924, Train Accuracy: 0.4963, Val Loss: 0.6967, Val Accuracy: 0.4873
Epoch 10/1000, Train Loss: 0.6920, Train Accuracy: 0.4977, Val Loss: 0.6966, Val Accuracy: 0.4873
Epoch 11/1000, Train Loss: 0.6916, Train Accuracy: 0.4985, Val Loss: 0.6965, Val Accuracy: 0.4873
Epoch 12/1000, Train Loss: 0.6914, Train Accuracy: 0.4977, Val Loss: 0.6964, Val Accuracy: 0.4873
Epoch 13/1000, Train Loss: 0.6895, Train Accuracy: 0.4999, Val Loss: 0.6963, Val Accuracy: 0.4873
Epoch 14/1000, Train Loss: 0.6890, Train Accuracy: 0.5001, Val Loss: 0.6961, Val Accuracy: 0.4873
Epoch 15/1000, Train Loss: 0.6880, Train Accuracy: 0.5015, Val Loss: 0.6960, Val Accuracy: 0.4873
Epoch 16/1000, Train Loss: 0.6879, Train Accuracy: 0.5026, Val Loss: 0.6959, Val Accuracy: 0.4873
Epoch 17/1000, Train Loss: 0.6867, Train Accuracy: 0.5049, Val Loss: 0.6957, Val Accuracy: 0.4873
Epoch 18/1000, Train Loss: 0.6860, Train Accuracy: 0.5043, Val Loss: 0.6955, Val Accuracy: 0.4873
Epoch 19/1000, Train Loss: 0.6859, Train Accuracy: 0.5036, Val Loss: 0.6954, Val Accuracy: 0.4873
Epoch 20/1000, Train Loss: 0.6848, Train Accuracy: 0.5080, Val Loss: 0.6952, Val Accuracy: 0.4873
Epoch 21/1000, Train Loss: 0.6840, Train Accuracy: 0.5082, Val Loss: 0.6950, Val Accuracy: 0.4873
Epoch 22/1000, Train Loss: 0.6829, Train Accuracy: 0.5095, Val Loss: 0.6948, Val Accuracy: 0.4873
Epoch 23/1000, Train Loss: 0.6830, Train Accuracy: 0.5106, Val Loss: 0.6945, Val Accuracy: 0.4873
Epoch 24/1000, Train Loss: 0.6816, Train Accuracy: 0.5134, Val Loss: 0.6943, Val Accuracy: 0.4871
Epoch 25/1000, Train Loss: 0.6815, Train Accuracy: 0.5127, Val Loss: 0.6940, Val Accuracy: 0.4871
Epoch 26/1000, Train Loss: 0.6800, Train Accuracy: 0.5192, Val Loss: 0.6938, Val Accuracy: 0.4867
Epoch 27/1000, Train Loss: 0.6793, Train Accuracy: 0.5190, Val Loss: 0.6934, Val Accuracy: 0.4867
Epoch 28/1000, Train Loss: 0.6788, Train Accuracy: 0.5226, Val Loss: 0.6931, Val Accuracy: 0.4867
Epoch 29/1000, Train Loss: 0.6784, Train Accuracy: 0.5192, Val Loss: 0.6928, Val Accuracy: 0.4867
Epoch 30/1000, Train Loss: 0.6775, Train Accuracy: 0.5245, Val Loss: 0.6924, Val Accuracy: 0.4867
Epoch 31/1000, Train Loss: 0.6768, Train Accuracy: 0.5277, Val Loss: 0.6920, Val Accuracy: 0.4864
Epoch 32/1000, Train Loss: 0.6758, Train Accuracy: 0.5299, Val Loss: 0.6915, Val Accuracy: 0.4864
Epoch 33/1000, Train Loss: 0.6751, Train Accuracy: 0.5279, Val Loss: 0.6911, Val Accuracy: 0.4862
Epoch 34/1000, Train Loss: 0.6741, Train Accuracy: 0.5335, Val Loss: 0.6906, Val Accuracy: 0.4861
Epoch 35/1000, Train Loss: 0.6743, Train Accuracy: 0.5353, Val Loss: 0.6900, Val Accuracy: 0.4861
Epoch 36/1000, Train Loss: 0.6736, Train Accuracy: 0.5385, Val Loss: 0.6895, Val Accuracy: 0.4866
Epoch 37/1000, Train Loss: 0.6723, Train Accuracy: 0.5437, Val Loss: 0.6889, Val Accuracy: 0.4871
Epoch 38/1000, Train Loss: 0.6714, Train Accuracy: 0.5458, Val Loss: 0.6882, Val Accuracy: 0.4874
Epoch 39/1000, Train Loss: 0.6709, Train Accuracy: 0.5450, Val Loss: 0.6875, Val Accuracy: 0.4876
Epoch 40/1000, Train Loss: 0.6699, Train Accuracy: 0.5501, Val Loss: 0.6868, Val Accuracy: 0.4890
Epoch 41/1000, Train Loss: 0.6688, Train Accuracy: 0.5525, Val Loss: 0.6860, Val Accuracy: 0.4897
Epoch 42/1000, Train Loss: 0.6682, Train Accuracy: 0.5532, Val Loss: 0.6852, Val Accuracy: 0.4907
Epoch 43/1000, Train Loss: 0.6683, Train Accuracy: 0.5566, Val Loss: 0.6843, Val Accuracy: 0.4926
Epoch 44/1000, Train Loss: 0.6671, Train Accuracy: 0.5603, Val Loss: 0.6834, Val Accuracy: 0.4941
Epoch 45/1000, Train Loss: 0.6666, Train Accuracy: 0.5609, Val Loss: 0.6824, Val Accuracy: 0.4964
Epoch 46/1000, Train Loss: 0.6652, Train Accuracy: 0.5666, Val Loss: 0.6814, Val Accuracy: 0.4972
Epoch 47/1000, Train Loss: 0.6650, Train Accuracy: 0.5691, Val Loss: 0.6804, Val Accuracy: 0.4988
Epoch 48/1000, Train Loss: 0.6642, Train Accuracy: 0.5721, Val Loss: 0.6793, Val Accuracy: 0.5026
Epoch 49/1000, Train Loss: 0.6635, Train Accuracy: 0.5716, Val Loss: 0.6781, Val Accuracy: 0.5052
Epoch 50/1000, Train Loss: 0.6622, Train Accuracy: 0.5768, Val Loss: 0.6770, Val Accuracy: 0.5084
Epoch 51/1000, Train Loss: 0.6619, Train Accuracy: 0.5775, Val Loss: 0.6758, Val Accuracy: 0.5134
Epoch 52/1000, Train Loss: 0.6605, Train Accuracy: 0.5835, Val Loss: 0.6745, Val Accuracy: 0.5174
Epoch 53/1000, Train Loss: 0.6602, Train Accuracy: 0.5848, Val Loss: 0.6732, Val Accuracy: 0.5215
Epoch 54/1000, Train Loss: 0.6591, Train Accuracy: 0.5905, Val Loss: 0.6719, Val Accuracy: 0.5243
Epoch 55/1000, Train Loss: 0.6585, Train Accuracy: 0.5930, Val Loss: 0.6705, Val Accuracy: 0.5299
Epoch 56/1000, Train Loss: 0.6578, Train Accuracy: 0.5966, Val Loss: 0.6692, Val Accuracy: 0.5344
Epoch 57/1000, Train Loss: 0.6566, Train Accuracy: 0.5989, Val Loss: 0.6678, Val Accuracy: 0.5408
Epoch 58/1000, Train Loss: 0.6560, Train Accuracy: 0.6046, Val Loss: 0.6664, Val Accuracy: 0.5472
Epoch 59/1000, Train Loss: 0.6550, Train Accuracy: 0.6051, Val Loss: 0.6650, Val Accuracy: 0.5530
Epoch 60/1000, Train Loss: 0.6543, Train Accuracy: 0.6064, Val Loss: 0.6635, Val Accuracy: 0.5577
Epoch 61/1000, Train Loss: 0.6534, Train Accuracy: 0.6146, Val Loss: 0.6621, Val Accuracy: 0.5637
Epoch 62/1000, Train Loss: 0.6529, Train Accuracy: 0.6143, Val Loss: 0.6606, Val Accuracy: 0.5716
Epoch 63/1000, Train Loss: 0.6526, Train Accuracy: 0.6176, Val Loss: 0.6591, Val Accuracy: 0.5806
Epoch 64/1000, Train Loss: 0.6504, Train Accuracy: 0.6234, Val Loss: 0.6577, Val Accuracy: 0.5862
Epoch 65/1000, Train Loss: 0.6497, Train Accuracy: 0.6239, Val Loss: 0.6562, Val Accuracy: 0.5935
Epoch 66/1000, Train Loss: 0.6493, Train Accuracy: 0.6240, Val Loss: 0.6548, Val Accuracy: 0.5990
Epoch 67/1000, Train Loss: 0.6484, Train Accuracy: 0.6268, Val Loss: 0.6534, Val Accuracy: 0.6060
Epoch 68/1000, Train Loss: 0.6480, Train Accuracy: 0.6336, Val Loss: 0.6519, Val Accuracy: 0.6105
Epoch 69/1000, Train Loss: 0.6463, Train Accuracy: 0.6378, Val Loss: 0.6505, Val Accuracy: 0.6157
Epoch 70/1000, Train Loss: 0.6453, Train Accuracy: 0.6373, Val Loss: 0.6491, Val Accuracy: 0.6217
Epoch 71/1000, Train Loss: 0.6450, Train Accuracy: 0.6419, Val Loss: 0.6477, Val Accuracy: 0.6299
Epoch 72/1000, Train Loss: 0.6435, Train Accuracy: 0.6493, Val Loss: 0.6463, Val Accuracy: 0.6351
Epoch 73/1000, Train Loss: 0.6428, Train Accuracy: 0.6519, Val Loss: 0.6449, Val Accuracy: 0.6423
Epoch 74/1000, Train Loss: 0.6417, Train Accuracy: 0.6528, Val Loss: 0.6435, Val Accuracy: 0.6504
Epoch 75/1000, Train Loss: 0.6408, Train Accuracy: 0.6592, Val Loss: 0.6422, Val Accuracy: 0.6578
Epoch 76/1000, Train Loss: 0.6396, Train Accuracy: 0.6617, Val Loss: 0.6409, Val Accuracy: 0.6635
Epoch 77/1000, Train Loss: 0.6390, Train Accuracy: 0.6631, Val Loss: 0.6395, Val Accuracy: 0.6682
Epoch 78/1000, Train Loss: 0.6379, Train Accuracy: 0.6668, Val Loss: 0.6382, Val Accuracy: 0.6730
Epoch 79/1000, Train Loss: 0.6368, Train Accuracy: 0.6746, Val Loss: 0.6369, Val Accuracy: 0.6797
Epoch 80/1000, Train Loss: 0.6363, Train Accuracy: 0.6743, Val Loss: 0.6356, Val Accuracy: 0.6850
Epoch 81/1000, Train Loss: 0.6343, Train Accuracy: 0.6799, Val Loss: 0.6343, Val Accuracy: 0.6919
Epoch 82/1000, Train Loss: 0.6336, Train Accuracy: 0.6825, Val Loss: 0.6330, Val Accuracy: 0.6971
Epoch 83/1000, Train Loss: 0.6328, Train Accuracy: 0.6826, Val Loss: 0.6318, Val Accuracy: 0.7034
Epoch 84/1000, Train Loss: 0.6325, Train Accuracy: 0.6817, Val Loss: 0.6305, Val Accuracy: 0.7107
Epoch 85/1000, Train Loss: 0.6309, Train Accuracy: 0.6899, Val Loss: 0.6292, Val Accuracy: 0.7169
Epoch 86/1000, Train Loss: 0.6292, Train Accuracy: 0.6936, Val Loss: 0.6279, Val Accuracy: 0.7219
Epoch 87/1000, Train Loss: 0.6284, Train Accuracy: 0.6959, Val Loss: 0.6267, Val Accuracy: 0.7255
Epoch 88/1000, Train Loss: 0.6280, Train Accuracy: 0.6955, Val Loss: 0.6254, Val Accuracy: 0.7296
Epoch 89/1000, Train Loss: 0.6260, Train Accuracy: 0.7027, Val Loss: 0.6241, Val Accuracy: 0.7330
Epoch 90/1000, Train Loss: 0.6253, Train Accuracy: 0.7022, Val Loss: 0.6229, Val Accuracy: 0.7356
Epoch 91/1000, Train Loss: 0.6237, Train Accuracy: 0.7081, Val Loss: 0.6216, Val Accuracy: 0.7386
Epoch 92/1000, Train Loss: 0.6229, Train Accuracy: 0.7118, Val Loss: 0.6203, Val Accuracy: 0.7415
Epoch 93/1000, Train Loss: 0.6216, Train Accuracy: 0.7158, Val Loss: 0.6191, Val Accuracy: 0.7437
Epoch 94/1000, Train Loss: 0.6201, Train Accuracy: 0.7200, Val Loss: 0.6178, Val Accuracy: 0.7480
Epoch 95/1000, Train Loss: 0.6188, Train Accuracy: 0.7218, Val Loss: 0.6165, Val Accuracy: 0.7520
Epoch 96/1000, Train Loss: 0.6185, Train Accuracy: 0.7211, Val Loss: 0.6152, Val Accuracy: 0.7556
Epoch 97/1000, Train Loss: 0.6171, Train Accuracy: 0.7221, Val Loss: 0.6139, Val Accuracy: 0.7587
Epoch 98/1000, Train Loss: 0.6156, Train Accuracy: 0.7271, Val Loss: 0.6126, Val Accuracy: 0.7616
Epoch 99/1000, Train Loss: 0.6143, Train Accuracy: 0.7317, Val Loss: 0.6113, Val Accuracy: 0.7645
Epoch 100/1000, Train Loss: 0.6132, Train Accuracy: 0.7306, Val Loss: 0.6100, Val Accuracy: 0.7678
Epoch 101/1000, Train Loss: 0.6118, Train Accuracy: 0.7375, Val Loss: 0.6086, Val Accuracy: 0.7713
Epoch 102/1000, Train Loss: 0.6102, Train Accuracy: 0.7421, Val Loss: 0.6073, Val Accuracy: 0.7744
Epoch 103/1000, Train Loss: 0.6096, Train Accuracy: 0.7413, Val Loss: 0.6059, Val Accuracy: 0.7776
Epoch 104/1000, Train Loss: 0.6077, Train Accuracy: 0.7445, Val Loss: 0.6046, Val Accuracy: 0.7812
Epoch 105/1000, Train Loss: 0.6071, Train Accuracy: 0.7474, Val Loss: 0.6032, Val Accuracy: 0.7843
Epoch 106/1000, Train Loss: 0.6047, Train Accuracy: 0.7530, Val Loss: 0.6018, Val Accuracy: 0.7871
Epoch 107/1000, Train Loss: 0.6034, Train Accuracy: 0.7526, Val Loss: 0.6004, Val Accuracy: 0.7898
Epoch 108/1000, Train Loss: 0.6025, Train Accuracy: 0.7588, Val Loss: 0.5990, Val Accuracy: 0.7924
Epoch 109/1000, Train Loss: 0.6018, Train Accuracy: 0.7586, Val Loss: 0.5975, Val Accuracy: 0.7943
Epoch 110/1000, Train Loss: 0.6003, Train Accuracy: 0.7621, Val Loss: 0.5961, Val Accuracy: 0.7959
Epoch 111/1000, Train Loss: 0.5982, Train Accuracy: 0.7650, Val Loss: 0.5947, Val Accuracy: 0.8005
Epoch 112/1000, Train Loss: 0.5971, Train Accuracy: 0.7624, Val Loss: 0.5932, Val Accuracy: 0.8040
Epoch 113/1000, Train Loss: 0.5958, Train Accuracy: 0.7627, Val Loss: 0.5917, Val Accuracy: 0.8046
Epoch 114/1000, Train Loss: 0.5944, Train Accuracy: 0.7663, Val Loss: 0.5902, Val Accuracy: 0.8067
Epoch 115/1000, Train Loss: 0.5939, Train Accuracy: 0.7692, Val Loss: 0.5887, Val Accuracy: 0.8091
Epoch 116/1000, Train Loss: 0.5915, Train Accuracy: 0.7734, Val Loss: 0.5872, Val Accuracy: 0.8112
Epoch 117/1000, Train Loss: 0.5896, Train Accuracy: 0.7752, Val Loss: 0.5857, Val Accuracy: 0.8131
Epoch 118/1000, Train Loss: 0.5881, Train Accuracy: 0.7778, Val Loss: 0.5841, Val Accuracy: 0.8157
Epoch 119/1000, Train Loss: 0.5873, Train Accuracy: 0.7798, Val Loss: 0.5825, Val Accuracy: 0.8165
Epoch 120/1000, Train Loss: 0.5850, Train Accuracy: 0.7831, Val Loss: 0.5810, Val Accuracy: 0.8172
Epoch 121/1000, Train Loss: 0.5829, Train Accuracy: 0.7849, Val Loss: 0.5794, Val Accuracy: 0.8196
Epoch 122/1000, Train Loss: 0.5823, Train Accuracy: 0.7843, Val Loss: 0.5778, Val Accuracy: 0.8215
Epoch 123/1000, Train Loss: 0.5802, Train Accuracy: 0.7890, Val Loss: 0.5762, Val Accuracy: 0.8231
Epoch 124/1000, Train Loss: 0.5792, Train Accuracy: 0.7876, Val Loss: 0.5745, Val Accuracy: 0.8250
Epoch 125/1000, Train Loss: 0.5776, Train Accuracy: 0.7910, Val Loss: 0.5728, Val Accuracy: 0.8263
Epoch 126/1000, Train Loss: 0.5752, Train Accuracy: 0.7940, Val Loss: 0.5711, Val Accuracy: 0.8270
Epoch 127/1000, Train Loss: 0.5739, Train Accuracy: 0.7913, Val Loss: 0.5694, Val Accuracy: 0.8284
Epoch 128/1000, Train Loss: 0.5726, Train Accuracy: 0.7973, Val Loss: 0.5678, Val Accuracy: 0.8301
Epoch 129/1000, Train Loss: 0.5709, Train Accuracy: 0.7981, Val Loss: 0.5660, Val Accuracy: 0.8320
Epoch 130/1000, Train Loss: 0.5688, Train Accuracy: 0.8012, Val Loss: 0.5643, Val Accuracy: 0.8336
Epoch 131/1000, Train Loss: 0.5666, Train Accuracy: 0.8024, Val Loss: 0.5625, Val Accuracy: 0.8349
Epoch 132/1000, Train Loss: 0.5659, Train Accuracy: 0.8016, Val Loss: 0.5607, Val Accuracy: 0.8358
Epoch 133/1000, Train Loss: 0.5640, Train Accuracy: 0.8054, Val Loss: 0.5590, Val Accuracy: 0.8367
Epoch 134/1000, Train Loss: 0.5619, Train Accuracy: 0.8050, Val Loss: 0.5572, Val Accuracy: 0.8367
Epoch 135/1000, Train Loss: 0.5600, Train Accuracy: 0.8091, Val Loss: 0.5553, Val Accuracy: 0.8370
Epoch 136/1000, Train Loss: 0.5591, Train Accuracy: 0.8083, Val Loss: 0.5535, Val Accuracy: 0.8367
Epoch 137/1000, Train Loss: 0.5569, Train Accuracy: 0.8119, Val Loss: 0.5517, Val Accuracy: 0.8377
Epoch 138/1000, Train Loss: 0.5551, Train Accuracy: 0.8098, Val Loss: 0.5498, Val Accuracy: 0.8396
Epoch 139/1000, Train Loss: 0.5534, Train Accuracy: 0.8152, Val Loss: 0.5479, Val Accuracy: 0.8398
Epoch 140/1000, Train Loss: 0.5513, Train Accuracy: 0.8174, Val Loss: 0.5460, Val Accuracy: 0.8415
Epoch 141/1000, Train Loss: 0.5487, Train Accuracy: 0.8168, Val Loss: 0.5441, Val Accuracy: 0.8432
Epoch 142/1000, Train Loss: 0.5475, Train Accuracy: 0.8151, Val Loss: 0.5421, Val Accuracy: 0.8446
Epoch 143/1000, Train Loss: 0.5453, Train Accuracy: 0.8170, Val Loss: 0.5401, Val Accuracy: 0.8460
Epoch 144/1000, Train Loss: 0.5433, Train Accuracy: 0.8191, Val Loss: 0.5381, Val Accuracy: 0.8470
Epoch 145/1000, Train Loss: 0.5414, Train Accuracy: 0.8215, Val Loss: 0.5361, Val Accuracy: 0.8473
Epoch 146/1000, Train Loss: 0.5400, Train Accuracy: 0.8255, Val Loss: 0.5342, Val Accuracy: 0.8485
Epoch 147/1000, Train Loss: 0.5380, Train Accuracy: 0.8219, Val Loss: 0.5321, Val Accuracy: 0.8499
Epoch 148/1000, Train Loss: 0.5351, Train Accuracy: 0.8265, Val Loss: 0.5301, Val Accuracy: 0.8496
Epoch 149/1000, Train Loss: 0.5330, Train Accuracy: 0.8265, Val Loss: 0.5280, Val Accuracy: 0.8506
Epoch 150/1000, Train Loss: 0.5307, Train Accuracy: 0.8291, Val Loss: 0.5260, Val Accuracy: 0.8518
Epoch 151/1000, Train Loss: 0.5288, Train Accuracy: 0.8300, Val Loss: 0.5238, Val Accuracy: 0.8518
Epoch 152/1000, Train Loss: 0.5271, Train Accuracy: 0.8286, Val Loss: 0.5217, Val Accuracy: 0.8528
Epoch 153/1000, Train Loss: 0.5248, Train Accuracy: 0.8331, Val Loss: 0.5196, Val Accuracy: 0.8534
Epoch 154/1000, Train Loss: 0.5230, Train Accuracy: 0.8325, Val Loss: 0.5175, Val Accuracy: 0.8540
Epoch 155/1000, Train Loss: 0.5213, Train Accuracy: 0.8325, Val Loss: 0.5153, Val Accuracy: 0.8551
Epoch 156/1000, Train Loss: 0.5188, Train Accuracy: 0.8347, Val Loss: 0.5132, Val Accuracy: 0.8563
Epoch 157/1000, Train Loss: 0.5160, Train Accuracy: 0.8383, Val Loss: 0.5110, Val Accuracy: 0.8578
Epoch 158/1000, Train Loss: 0.5151, Train Accuracy: 0.8369, Val Loss: 0.5088, Val Accuracy: 0.8585
Epoch 159/1000, Train Loss: 0.5135, Train Accuracy: 0.8382, Val Loss: 0.5067, Val Accuracy: 0.8599
Epoch 160/1000, Train Loss: 0.5105, Train Accuracy: 0.8408, Val Loss: 0.5045, Val Accuracy: 0.8614
Epoch 161/1000, Train Loss: 0.5092, Train Accuracy: 0.8395, Val Loss: 0.5023, Val Accuracy: 0.8627
Epoch 162/1000, Train Loss: 0.5057, Train Accuracy: 0.8452, Val Loss: 0.5001, Val Accuracy: 0.8644
Epoch 163/1000, Train Loss: 0.5045, Train Accuracy: 0.8430, Val Loss: 0.4978, Val Accuracy: 0.8661
Epoch 164/1000, Train Loss: 0.5019, Train Accuracy: 0.8442, Val Loss: 0.4955, Val Accuracy: 0.8666
Epoch 165/1000, Train Loss: 0.4994, Train Accuracy: 0.8465, Val Loss: 0.4932, Val Accuracy: 0.8670
Epoch 166/1000, Train Loss: 0.4970, Train Accuracy: 0.8461, Val Loss: 0.4909, Val Accuracy: 0.8670
Epoch 167/1000, Train Loss: 0.4954, Train Accuracy: 0.8454, Val Loss: 0.4886, Val Accuracy: 0.8675
Epoch 168/1000, Train Loss: 0.4927, Train Accuracy: 0.8474, Val Loss: 0.4862, Val Accuracy: 0.8695
Epoch 169/1000, Train Loss: 0.4910, Train Accuracy: 0.8492, Val Loss: 0.4839, Val Accuracy: 0.8707
Epoch 170/1000, Train Loss: 0.4881, Train Accuracy: 0.8481, Val Loss: 0.4815, Val Accuracy: 0.8718
Epoch 171/1000, Train Loss: 0.4862, Train Accuracy: 0.8513, Val Loss: 0.4792, Val Accuracy: 0.8721
Epoch 172/1000, Train Loss: 0.4848, Train Accuracy: 0.8491, Val Loss: 0.4769, Val Accuracy: 0.8728
Epoch 173/1000, Train Loss: 0.4813, Train Accuracy: 0.8542, Val Loss: 0.4745, Val Accuracy: 0.8749
Epoch 174/1000, Train Loss: 0.4788, Train Accuracy: 0.8553, Val Loss: 0.4722, Val Accuracy: 0.8752
Epoch 175/1000, Train Loss: 0.4763, Train Accuracy: 0.8525, Val Loss: 0.4698, Val Accuracy: 0.8756
Epoch 176/1000, Train Loss: 0.4743, Train Accuracy: 0.8554, Val Loss: 0.4674, Val Accuracy: 0.8761
Epoch 177/1000, Train Loss: 0.4716, Train Accuracy: 0.8586, Val Loss: 0.4650, Val Accuracy: 0.8778
Epoch 178/1000, Train Loss: 0.4694, Train Accuracy: 0.8573, Val Loss: 0.4626, Val Accuracy: 0.8788
Epoch 179/1000, Train Loss: 0.4673, Train Accuracy: 0.8577, Val Loss: 0.4602, Val Accuracy: 0.8797
Epoch 180/1000, Train Loss: 0.4663, Train Accuracy: 0.8545, Val Loss: 0.4578, Val Accuracy: 0.8809
Epoch 181/1000, Train Loss: 0.4622, Train Accuracy: 0.8589, Val Loss: 0.4554, Val Accuracy: 0.8821
Epoch 182/1000, Train Loss: 0.4612, Train Accuracy: 0.8590, Val Loss: 0.4530, Val Accuracy: 0.8828
Epoch 183/1000, Train Loss: 0.4587, Train Accuracy: 0.8607, Val Loss: 0.4505, Val Accuracy: 0.8833
Epoch 184/1000, Train Loss: 0.4551, Train Accuracy: 0.8637, Val Loss: 0.4481, Val Accuracy: 0.8842
Epoch 185/1000, Train Loss: 0.4532, Train Accuracy: 0.8632, Val Loss: 0.4457, Val Accuracy: 0.8845
Epoch 186/1000, Train Loss: 0.4517, Train Accuracy: 0.8626, Val Loss: 0.4433, Val Accuracy: 0.8847
Epoch 187/1000, Train Loss: 0.4499, Train Accuracy: 0.8632, Val Loss: 0.4408, Val Accuracy: 0.8855
Epoch 188/1000, Train Loss: 0.4470, Train Accuracy: 0.8659, Val Loss: 0.4384, Val Accuracy: 0.8871
Epoch 189/1000, Train Loss: 0.4426, Train Accuracy: 0.8710, Val Loss: 0.4359, Val Accuracy: 0.8878
Epoch 190/1000, Train Loss: 0.4419, Train Accuracy: 0.8661, Val Loss: 0.4335, Val Accuracy: 0.8880
Epoch 191/1000, Train Loss: 0.4394, Train Accuracy: 0.8688, Val Loss: 0.4311, Val Accuracy: 0.8886
Epoch 192/1000, Train Loss: 0.4374, Train Accuracy: 0.8670, Val Loss: 0.4286, Val Accuracy: 0.8892
Epoch 193/1000, Train Loss: 0.4343, Train Accuracy: 0.8684, Val Loss: 0.4262, Val Accuracy: 0.8900
Epoch 194/1000, Train Loss: 0.4311, Train Accuracy: 0.8703, Val Loss: 0.4237, Val Accuracy: 0.8904
Epoch 195/1000, Train Loss: 0.4302, Train Accuracy: 0.8722, Val Loss: 0.4213, Val Accuracy: 0.8909
Epoch 196/1000, Train Loss: 0.4266, Train Accuracy: 0.8737, Val Loss: 0.4189, Val Accuracy: 0.8914
Epoch 197/1000, Train Loss: 0.4250, Train Accuracy: 0.8727, Val Loss: 0.4164, Val Accuracy: 0.8912
Epoch 198/1000, Train Loss: 0.4230, Train Accuracy: 0.8741, Val Loss: 0.4140, Val Accuracy: 0.8921
Epoch 199/1000, Train Loss: 0.4211, Train Accuracy: 0.8751, Val Loss: 0.4116, Val Accuracy: 0.8928
Epoch 200/1000, Train Loss: 0.4175, Train Accuracy: 0.8774, Val Loss: 0.4092, Val Accuracy: 0.8931
Epoch 201/1000, Train Loss: 0.4160, Train Accuracy: 0.8747, Val Loss: 0.4068, Val Accuracy: 0.8929
Epoch 202/1000, Train Loss: 0.4137, Train Accuracy: 0.8770, Val Loss: 0.4044, Val Accuracy: 0.8936
Epoch 203/1000, Train Loss: 0.4108, Train Accuracy: 0.8786, Val Loss: 0.4021, Val Accuracy: 0.8943
Epoch 204/1000, Train Loss: 0.4087, Train Accuracy: 0.8779, Val Loss: 0.3996, Val Accuracy: 0.8950
Epoch 205/1000, Train Loss: 0.4068, Train Accuracy: 0.8800, Val Loss: 0.3972, Val Accuracy: 0.8954
Epoch 206/1000, Train Loss: 0.4045, Train Accuracy: 0.8813, Val Loss: 0.3949, Val Accuracy: 0.8957
Epoch 207/1000, Train Loss: 0.4012, Train Accuracy: 0.8801, Val Loss: 0.3925, Val Accuracy: 0.8955
Epoch 208/1000, Train Loss: 0.4003, Train Accuracy: 0.8806, Val Loss: 0.3901, Val Accuracy: 0.8962
Epoch 209/1000, Train Loss: 0.3969, Train Accuracy: 0.8841, Val Loss: 0.3877, Val Accuracy: 0.8971
Epoch 210/1000, Train Loss: 0.3959, Train Accuracy: 0.8820, Val Loss: 0.3854, Val Accuracy: 0.8976
Epoch 211/1000, Train Loss: 0.3925, Train Accuracy: 0.8840, Val Loss: 0.3831, Val Accuracy: 0.8983
Epoch 212/1000, Train Loss: 0.3915, Train Accuracy: 0.8814, Val Loss: 0.3807, Val Accuracy: 0.8988
Epoch 213/1000, Train Loss: 0.3886, Train Accuracy: 0.8831, Val Loss: 0.3784, Val Accuracy: 0.8991
Epoch 214/1000, Train Loss: 0.3861, Train Accuracy: 0.8866, Val Loss: 0.3760, Val Accuracy: 0.8998
Epoch 215/1000, Train Loss: 0.3838, Train Accuracy: 0.8877, Val Loss: 0.3738, Val Accuracy: 0.9003
Epoch 216/1000, Train Loss: 0.3814, Train Accuracy: 0.8889, Val Loss: 0.3714, Val Accuracy: 0.9007
Epoch 217/1000, Train Loss: 0.3784, Train Accuracy: 0.8866, Val Loss: 0.3691, Val Accuracy: 0.9014
Epoch 218/1000, Train Loss: 0.3787, Train Accuracy: 0.8875, Val Loss: 0.3668, Val Accuracy: 0.9019
Epoch 219/1000, Train Loss: 0.3749, Train Accuracy: 0.8884, Val Loss: 0.3645, Val Accuracy: 0.9028
Epoch 220/1000, Train Loss: 0.3728, Train Accuracy: 0.8912, Val Loss: 0.3622, Val Accuracy: 0.9034
Epoch 221/1000, Train Loss: 0.3706, Train Accuracy: 0.8901, Val Loss: 0.3600, Val Accuracy: 0.9038
Epoch 222/1000, Train Loss: 0.3680, Train Accuracy: 0.8898, Val Loss: 0.3578, Val Accuracy: 0.9045
Epoch 223/1000, Train Loss: 0.3649, Train Accuracy: 0.8946, Val Loss: 0.3555, Val Accuracy: 0.9048
Epoch 224/1000, Train Loss: 0.3636, Train Accuracy: 0.8905, Val Loss: 0.3533, Val Accuracy: 0.9055
Epoch 225/1000, Train Loss: 0.3618, Train Accuracy: 0.8923, Val Loss: 0.3511, Val Accuracy: 0.9062
Epoch 226/1000, Train Loss: 0.3608, Train Accuracy: 0.8922, Val Loss: 0.3489, Val Accuracy: 0.9062
Epoch 227/1000, Train Loss: 0.3580, Train Accuracy: 0.8919, Val Loss: 0.3467, Val Accuracy: 0.9064
Epoch 228/1000, Train Loss: 0.3544, Train Accuracy: 0.8958, Val Loss: 0.3445, Val Accuracy: 0.9067
Epoch 229/1000, Train Loss: 0.3541, Train Accuracy: 0.8942, Val Loss: 0.3424, Val Accuracy: 0.9072
Epoch 230/1000, Train Loss: 0.3506, Train Accuracy: 0.8943, Val Loss: 0.3402, Val Accuracy: 0.9081
Epoch 231/1000, Train Loss: 0.3475, Train Accuracy: 0.8962, Val Loss: 0.3381, Val Accuracy: 0.9084
Epoch 232/1000, Train Loss: 0.3463, Train Accuracy: 0.8976, Val Loss: 0.3360, Val Accuracy: 0.9083
Epoch 233/1000, Train Loss: 0.3429, Train Accuracy: 0.8983, Val Loss: 0.3339, Val Accuracy: 0.9079
Epoch 234/1000, Train Loss: 0.3419, Train Accuracy: 0.8991, Val Loss: 0.3317, Val Accuracy: 0.9081
Epoch 235/1000, Train Loss: 0.3403, Train Accuracy: 0.8966, Val Loss: 0.3296, Val Accuracy: 0.9084
Epoch 236/1000, Train Loss: 0.3401, Train Accuracy: 0.8958, Val Loss: 0.3275, Val Accuracy: 0.9086
Epoch 237/1000, Train Loss: 0.3370, Train Accuracy: 0.8981, Val Loss: 0.3255, Val Accuracy: 0.9090
Epoch 238/1000, Train Loss: 0.3348, Train Accuracy: 0.8996, Val Loss: 0.3235, Val Accuracy: 0.9096
Epoch 239/1000, Train Loss: 0.3326, Train Accuracy: 0.9003, Val Loss: 0.3215, Val Accuracy: 0.9105
Epoch 240/1000, Train Loss: 0.3301, Train Accuracy: 0.8999, Val Loss: 0.3195, Val Accuracy: 0.9105
Epoch 241/1000, Train Loss: 0.3290, Train Accuracy: 0.9025, Val Loss: 0.3175, Val Accuracy: 0.9107
Epoch 242/1000, Train Loss: 0.3257, Train Accuracy: 0.9009, Val Loss: 0.3155, Val Accuracy: 0.9112
Epoch 243/1000, Train Loss: 0.3247, Train Accuracy: 0.9026, Val Loss: 0.3136, Val Accuracy: 0.9114
Epoch 244/1000, Train Loss: 0.3229, Train Accuracy: 0.9022, Val Loss: 0.3116, Val Accuracy: 0.9120
Epoch 245/1000, Train Loss: 0.3199, Train Accuracy: 0.9033, Val Loss: 0.3097, Val Accuracy: 0.9120
Epoch 246/1000, Train Loss: 0.3185, Train Accuracy: 0.9057, Val Loss: 0.3078, Val Accuracy: 0.9131
Epoch 247/1000, Train Loss: 0.3164, Train Accuracy: 0.9053, Val Loss: 0.3058, Val Accuracy: 0.9131
Epoch 248/1000, Train Loss: 0.3160, Train Accuracy: 0.9054, Val Loss: 0.3040, Val Accuracy: 0.9133
Epoch 249/1000, Train Loss: 0.3139, Train Accuracy: 0.9055, Val Loss: 0.3021, Val Accuracy: 0.9138
Epoch 250/1000, Train Loss: 0.3125, Train Accuracy: 0.9042, Val Loss: 0.3003, Val Accuracy: 0.9141
Epoch 251/1000, Train Loss: 0.3089, Train Accuracy: 0.9057, Val Loss: 0.2985, Val Accuracy: 0.9143
Epoch 252/1000, Train Loss: 0.3072, Train Accuracy: 0.9069, Val Loss: 0.2967, Val Accuracy: 0.9146
Epoch 253/1000, Train Loss: 0.3076, Train Accuracy: 0.9065, Val Loss: 0.2949, Val Accuracy: 0.9148
Epoch 254/1000, Train Loss: 0.3046, Train Accuracy: 0.9072, Val Loss: 0.2931, Val Accuracy: 0.9150
Epoch 255/1000, Train Loss: 0.3030, Train Accuracy: 0.9078, Val Loss: 0.2915, Val Accuracy: 0.9151
Epoch 256/1000, Train Loss: 0.3011, Train Accuracy: 0.9083, Val Loss: 0.2897, Val Accuracy: 0.9155
Epoch 257/1000, Train Loss: 0.2993, Train Accuracy: 0.9083, Val Loss: 0.2880, Val Accuracy: 0.9153
Epoch 258/1000, Train Loss: 0.2980, Train Accuracy: 0.9081, Val Loss: 0.2863, Val Accuracy: 0.9155
Epoch 259/1000, Train Loss: 0.2964, Train Accuracy: 0.9100, Val Loss: 0.2846, Val Accuracy: 0.9158
Epoch 260/1000, Train Loss: 0.2944, Train Accuracy: 0.9092, Val Loss: 0.2830, Val Accuracy: 0.9164
Epoch 261/1000, Train Loss: 0.2936, Train Accuracy: 0.9111, Val Loss: 0.2813, Val Accuracy: 0.9172
Epoch 262/1000, Train Loss: 0.2905, Train Accuracy: 0.9100, Val Loss: 0.2797, Val Accuracy: 0.9172
Epoch 263/1000, Train Loss: 0.2877, Train Accuracy: 0.9112, Val Loss: 0.2781, Val Accuracy: 0.9176
Epoch 264/1000, Train Loss: 0.2884, Train Accuracy: 0.9110, Val Loss: 0.2765, Val Accuracy: 0.9184
Epoch 265/1000, Train Loss: 0.2861, Train Accuracy: 0.9107, Val Loss: 0.2749, Val Accuracy: 0.9184
Epoch 266/1000, Train Loss: 0.2835, Train Accuracy: 0.9145, Val Loss: 0.2734, Val Accuracy: 0.9191
Epoch 267/1000, Train Loss: 0.2831, Train Accuracy: 0.9124, Val Loss: 0.2718, Val Accuracy: 0.9196
Epoch 268/1000, Train Loss: 0.2803, Train Accuracy: 0.9138, Val Loss: 0.2703, Val Accuracy: 0.9196
Epoch 269/1000, Train Loss: 0.2819, Train Accuracy: 0.9126, Val Loss: 0.2687, Val Accuracy: 0.9201
Epoch 270/1000, Train Loss: 0.2786, Train Accuracy: 0.9135, Val Loss: 0.2673, Val Accuracy: 0.9210
Epoch 271/1000, Train Loss: 0.2772, Train Accuracy: 0.9143, Val Loss: 0.2658, Val Accuracy: 0.9212
Epoch 272/1000, Train Loss: 0.2760, Train Accuracy: 0.9126, Val Loss: 0.2644, Val Accuracy: 0.9212
Epoch 273/1000, Train Loss: 0.2737, Train Accuracy: 0.9149, Val Loss: 0.2629, Val Accuracy: 0.9213
Epoch 274/1000, Train Loss: 0.2729, Train Accuracy: 0.9145, Val Loss: 0.2615, Val Accuracy: 0.9217
Epoch 275/1000, Train Loss: 0.2709, Train Accuracy: 0.9151, Val Loss: 0.2600, Val Accuracy: 0.9217
Epoch 276/1000, Train Loss: 0.2700, Train Accuracy: 0.9156, Val Loss: 0.2586, Val Accuracy: 0.9217
Epoch 277/1000, Train Loss: 0.2692, Train Accuracy: 0.9166, Val Loss: 0.2572, Val Accuracy: 0.9222
Epoch 278/1000, Train Loss: 0.2682, Train Accuracy: 0.9139, Val Loss: 0.2559, Val Accuracy: 0.9222
Epoch 279/1000, Train Loss: 0.2661, Train Accuracy: 0.9172, Val Loss: 0.2545, Val Accuracy: 0.9222
Epoch 280/1000, Train Loss: 0.2649, Train Accuracy: 0.9159, Val Loss: 0.2532, Val Accuracy: 0.9225
Epoch 281/1000, Train Loss: 0.2648, Train Accuracy: 0.9161, Val Loss: 0.2519, Val Accuracy: 0.9234
Epoch 282/1000, Train Loss: 0.2628, Train Accuracy: 0.9156, Val Loss: 0.2507, Val Accuracy: 0.9238
Epoch 283/1000, Train Loss: 0.2605, Train Accuracy: 0.9171, Val Loss: 0.2494, Val Accuracy: 0.9241
Epoch 284/1000, Train Loss: 0.2593, Train Accuracy: 0.9169, Val Loss: 0.2481, Val Accuracy: 0.9241
Epoch 285/1000, Train Loss: 0.2584, Train Accuracy: 0.9181, Val Loss: 0.2468, Val Accuracy: 0.9241
Epoch 286/1000, Train Loss: 0.2564, Train Accuracy: 0.9189, Val Loss: 0.2455, Val Accuracy: 0.9246
Epoch 287/1000, Train Loss: 0.2572, Train Accuracy: 0.9166, Val Loss: 0.2443, Val Accuracy: 0.9251
Epoch 288/1000, Train Loss: 0.2530, Train Accuracy: 0.9206, Val Loss: 0.2431, Val Accuracy: 0.9250
Epoch 289/1000, Train Loss: 0.2523, Train Accuracy: 0.9193, Val Loss: 0.2418, Val Accuracy: 0.9251
Epoch 290/1000, Train Loss: 0.2516, Train Accuracy: 0.9206, Val Loss: 0.2406, Val Accuracy: 0.9251
Epoch 291/1000, Train Loss: 0.2492, Train Accuracy: 0.9187, Val Loss: 0.2394, Val Accuracy: 0.9256
Epoch 292/1000, Train Loss: 0.2496, Train Accuracy: 0.9214, Val Loss: 0.2383, Val Accuracy: 0.9260
Epoch 293/1000, Train Loss: 0.2482, Train Accuracy: 0.9191, Val Loss: 0.2372, Val Accuracy: 0.9262
Epoch 294/1000, Train Loss: 0.2480, Train Accuracy: 0.9187, Val Loss: 0.2361, Val Accuracy: 0.9262
Epoch 295/1000, Train Loss: 0.2448, Train Accuracy: 0.9224, Val Loss: 0.2349, Val Accuracy: 0.9260
Epoch 296/1000, Train Loss: 0.2447, Train Accuracy: 0.9212, Val Loss: 0.2338, Val Accuracy: 0.9265
Epoch 297/1000, Train Loss: 0.2443, Train Accuracy: 0.9209, Val Loss: 0.2327, Val Accuracy: 0.9267
Epoch 298/1000, Train Loss: 0.2448, Train Accuracy: 0.9210, Val Loss: 0.2316, Val Accuracy: 0.9267
Epoch 299/1000, Train Loss: 0.2406, Train Accuracy: 0.9208, Val Loss: 0.2304, Val Accuracy: 0.9269
Epoch 300/1000, Train Loss: 0.2385, Train Accuracy: 0.9237, Val Loss: 0.2294, Val Accuracy: 0.9275
Epoch 301/1000, Train Loss: 0.2379, Train Accuracy: 0.9226, Val Loss: 0.2283, Val Accuracy: 0.9277
Epoch 302/1000, Train Loss: 0.2387, Train Accuracy: 0.9201, Val Loss: 0.2273, Val Accuracy: 0.9277
Epoch 303/1000, Train Loss: 0.2375, Train Accuracy: 0.9237, Val Loss: 0.2262, Val Accuracy: 0.9284
Epoch 304/1000, Train Loss: 0.2353, Train Accuracy: 0.9236, Val Loss: 0.2252, Val Accuracy: 0.9281
Epoch 305/1000, Train Loss: 0.2359, Train Accuracy: 0.9223, Val Loss: 0.2243, Val Accuracy: 0.9279
Epoch 306/1000, Train Loss: 0.2338, Train Accuracy: 0.9237, Val Loss: 0.2233, Val Accuracy: 0.9284
Epoch 307/1000, Train Loss: 0.2316, Train Accuracy: 0.9250, Val Loss: 0.2222, Val Accuracy: 0.9282
Epoch 308/1000, Train Loss: 0.2317, Train Accuracy: 0.9249, Val Loss: 0.2212, Val Accuracy: 0.9282
Epoch 309/1000, Train Loss: 0.2318, Train Accuracy: 0.9244, Val Loss: 0.2203, Val Accuracy: 0.9279
Epoch 310/1000, Train Loss: 0.2304, Train Accuracy: 0.9237, Val Loss: 0.2194, Val Accuracy: 0.9284
Epoch 311/1000, Train Loss: 0.2291, Train Accuracy: 0.9240, Val Loss: 0.2184, Val Accuracy: 0.9289
Epoch 312/1000, Train Loss: 0.2275, Train Accuracy: 0.9249, Val Loss: 0.2175, Val Accuracy: 0.9293
Epoch 313/1000, Train Loss: 0.2271, Train Accuracy: 0.9264, Val Loss: 0.2166, Val Accuracy: 0.9293
Epoch 314/1000, Train Loss: 0.2268, Train Accuracy: 0.9237, Val Loss: 0.2157, Val Accuracy: 0.9298
Epoch 315/1000, Train Loss: 0.2243, Train Accuracy: 0.9266, Val Loss: 0.2148, Val Accuracy: 0.9298
Epoch 316/1000, Train Loss: 0.2236, Train Accuracy: 0.9265, Val Loss: 0.2139, Val Accuracy: 0.9299
Epoch 317/1000, Train Loss: 0.2223, Train Accuracy: 0.9277, Val Loss: 0.2129, Val Accuracy: 0.9301
Epoch 318/1000, Train Loss: 0.2229, Train Accuracy: 0.9266, Val Loss: 0.2120, Val Accuracy: 0.9301
Epoch 319/1000, Train Loss: 0.2207, Train Accuracy: 0.9284, Val Loss: 0.2111, Val Accuracy: 0.9305
Epoch 320/1000, Train Loss: 0.2202, Train Accuracy: 0.9275, Val Loss: 0.2102, Val Accuracy: 0.9306
Epoch 321/1000, Train Loss: 0.2184, Train Accuracy: 0.9290, Val Loss: 0.2094, Val Accuracy: 0.9308
Epoch 322/1000, Train Loss: 0.2203, Train Accuracy: 0.9288, Val Loss: 0.2085, Val Accuracy: 0.9315
Epoch 323/1000, Train Loss: 0.2176, Train Accuracy: 0.9289, Val Loss: 0.2077, Val Accuracy: 0.9318
Epoch 324/1000, Train Loss: 0.2184, Train Accuracy: 0.9261, Val Loss: 0.2070, Val Accuracy: 0.9324
Epoch 325/1000, Train Loss: 0.2143, Train Accuracy: 0.9289, Val Loss: 0.2061, Val Accuracy: 0.9325
Epoch 326/1000, Train Loss: 0.2157, Train Accuracy: 0.9272, Val Loss: 0.2053, Val Accuracy: 0.9327
Epoch 327/1000, Train Loss: 0.2138, Train Accuracy: 0.9286, Val Loss: 0.2045, Val Accuracy: 0.9327
Epoch 328/1000, Train Loss: 0.2130, Train Accuracy: 0.9299, Val Loss: 0.2037, Val Accuracy: 0.9329
Epoch 329/1000, Train Loss: 0.2118, Train Accuracy: 0.9302, Val Loss: 0.2030, Val Accuracy: 0.9327
Epoch 330/1000, Train Loss: 0.2112, Train Accuracy: 0.9303, Val Loss: 0.2022, Val Accuracy: 0.9329
Epoch 331/1000, Train Loss: 0.2108, Train Accuracy: 0.9290, Val Loss: 0.2014, Val Accuracy: 0.9329
Epoch 332/1000, Train Loss: 0.2103, Train Accuracy: 0.9294, Val Loss: 0.2007, Val Accuracy: 0.9336
Epoch 333/1000, Train Loss: 0.2108, Train Accuracy: 0.9297, Val Loss: 0.1999, Val Accuracy: 0.9341
Epoch 334/1000, Train Loss: 0.2101, Train Accuracy: 0.9302, Val Loss: 0.1992, Val Accuracy: 0.9343
Epoch 335/1000, Train Loss: 0.2069, Train Accuracy: 0.9299, Val Loss: 0.1985, Val Accuracy: 0.9344
Epoch 336/1000, Train Loss: 0.2066, Train Accuracy: 0.9311, Val Loss: 0.1977, Val Accuracy: 0.9344
Epoch 337/1000, Train Loss: 0.2052, Train Accuracy: 0.9315, Val Loss: 0.1970, Val Accuracy: 0.9344
Epoch 338/1000, Train Loss: 0.2044, Train Accuracy: 0.9314, Val Loss: 0.1963, Val Accuracy: 0.9348
Epoch 339/1000, Train Loss: 0.2053, Train Accuracy: 0.9332, Val Loss: 0.1956, Val Accuracy: 0.9349
Epoch 340/1000, Train Loss: 0.2030, Train Accuracy: 0.9335, Val Loss: 0.1949, Val Accuracy: 0.9351
Epoch 341/1000, Train Loss: 0.2038, Train Accuracy: 0.9311, Val Loss: 0.1942, Val Accuracy: 0.9348
Epoch 342/1000, Train Loss: 0.2012, Train Accuracy: 0.9334, Val Loss: 0.1935, Val Accuracy: 0.9349
Epoch 343/1000, Train Loss: 0.2017, Train Accuracy: 0.9333, Val Loss: 0.1929, Val Accuracy: 0.9351
Epoch 344/1000, Train Loss: 0.2009, Train Accuracy: 0.9330, Val Loss: 0.1922, Val Accuracy: 0.9355
Epoch 345/1000, Train Loss: 0.2002, Train Accuracy: 0.9334, Val Loss: 0.1915, Val Accuracy: 0.9360
Epoch 346/1000, Train Loss: 0.2003, Train Accuracy: 0.9327, Val Loss: 0.1909, Val Accuracy: 0.9360
Epoch 347/1000, Train Loss: 0.1983, Train Accuracy: 0.9336, Val Loss: 0.1902, Val Accuracy: 0.9360
Epoch 348/1000, Train Loss: 0.1994, Train Accuracy: 0.9319, Val Loss: 0.1896, Val Accuracy: 0.9360
Epoch 349/1000, Train Loss: 0.1969, Train Accuracy: 0.9334, Val Loss: 0.1889, Val Accuracy: 0.9361
Epoch 350/1000, Train Loss: 0.1965, Train Accuracy: 0.9326, Val Loss: 0.1883, Val Accuracy: 0.9367
Epoch 351/1000, Train Loss: 0.1953, Train Accuracy: 0.9333, Val Loss: 0.1876, Val Accuracy: 0.9370
Epoch 352/1000, Train Loss: 0.1965, Train Accuracy: 0.9336, Val Loss: 0.1870, Val Accuracy: 0.9370
Epoch 353/1000, Train Loss: 0.1946, Train Accuracy: 0.9348, Val Loss: 0.1864, Val Accuracy: 0.9368
Epoch 354/1000, Train Loss: 0.1937, Train Accuracy: 0.9339, Val Loss: 0.1858, Val Accuracy: 0.9375
Epoch 355/1000, Train Loss: 0.1944, Train Accuracy: 0.9329, Val Loss: 0.1852, Val Accuracy: 0.9375
Epoch 356/1000, Train Loss: 0.1922, Train Accuracy: 0.9346, Val Loss: 0.1846, Val Accuracy: 0.9382
Epoch 357/1000, Train Loss: 0.1925, Train Accuracy: 0.9347, Val Loss: 0.1840, Val Accuracy: 0.9382
Epoch 358/1000, Train Loss: 0.1913, Train Accuracy: 0.9358, Val Loss: 0.1835, Val Accuracy: 0.9384
Epoch 359/1000, Train Loss: 0.1909, Train Accuracy: 0.9367, Val Loss: 0.1828, Val Accuracy: 0.9384
Epoch 360/1000, Train Loss: 0.1894, Train Accuracy: 0.9362, Val Loss: 0.1822, Val Accuracy: 0.9387
Epoch 361/1000, Train Loss: 0.1905, Train Accuracy: 0.9348, Val Loss: 0.1817, Val Accuracy: 0.9387
Epoch 362/1000, Train Loss: 0.1889, Train Accuracy: 0.9370, Val Loss: 0.1811, Val Accuracy: 0.9389
Epoch 363/1000, Train Loss: 0.1888, Train Accuracy: 0.9360, Val Loss: 0.1805, Val Accuracy: 0.9389
Epoch 364/1000, Train Loss: 0.1889, Train Accuracy: 0.9356, Val Loss: 0.1799, Val Accuracy: 0.9392
Epoch 365/1000, Train Loss: 0.1860, Train Accuracy: 0.9373, Val Loss: 0.1794, Val Accuracy: 0.9392
Epoch 366/1000, Train Loss: 0.1864, Train Accuracy: 0.9367, Val Loss: 0.1788, Val Accuracy: 0.9394
Epoch 367/1000, Train Loss: 0.1864, Train Accuracy: 0.9365, Val Loss: 0.1783, Val Accuracy: 0.9396
Epoch 368/1000, Train Loss: 0.1865, Train Accuracy: 0.9362, Val Loss: 0.1778, Val Accuracy: 0.9394
Epoch 369/1000, Train Loss: 0.1846, Train Accuracy: 0.9379, Val Loss: 0.1773, Val Accuracy: 0.9396
Epoch 370/1000, Train Loss: 0.1858, Train Accuracy: 0.9360, Val Loss: 0.1768, Val Accuracy: 0.9398
Epoch 371/1000, Train Loss: 0.1837, Train Accuracy: 0.9378, Val Loss: 0.1763, Val Accuracy: 0.9396
Epoch 372/1000, Train Loss: 0.1853, Train Accuracy: 0.9376, Val Loss: 0.1759, Val Accuracy: 0.9398
Epoch 373/1000, Train Loss: 0.1844, Train Accuracy: 0.9362, Val Loss: 0.1753, Val Accuracy: 0.9401
Epoch 374/1000, Train Loss: 0.1829, Train Accuracy: 0.9385, Val Loss: 0.1748, Val Accuracy: 0.9401
Epoch 375/1000, Train Loss: 0.1811, Train Accuracy: 0.9388, Val Loss: 0.1743, Val Accuracy: 0.9403
Epoch 376/1000, Train Loss: 0.1819, Train Accuracy: 0.9388, Val Loss: 0.1738, Val Accuracy: 0.9408
Epoch 377/1000, Train Loss: 0.1806, Train Accuracy: 0.9379, Val Loss: 0.1733, Val Accuracy: 0.9406
Epoch 378/1000, Train Loss: 0.1816, Train Accuracy: 0.9384, Val Loss: 0.1728, Val Accuracy: 0.9408
Epoch 379/1000, Train Loss: 0.1806, Train Accuracy: 0.9382, Val Loss: 0.1724, Val Accuracy: 0.9408
Epoch 380/1000, Train Loss: 0.1780, Train Accuracy: 0.9402, Val Loss: 0.1719, Val Accuracy: 0.9408
Epoch 381/1000, Train Loss: 0.1772, Train Accuracy: 0.9416, Val Loss: 0.1714, Val Accuracy: 0.9408
Epoch 382/1000, Train Loss: 0.1791, Train Accuracy: 0.9386, Val Loss: 0.1709, Val Accuracy: 0.9408
Epoch 383/1000, Train Loss: 0.1775, Train Accuracy: 0.9379, Val Loss: 0.1704, Val Accuracy: 0.9408
Epoch 384/1000, Train Loss: 0.1760, Train Accuracy: 0.9398, Val Loss: 0.1700, Val Accuracy: 0.9410
Epoch 385/1000, Train Loss: 0.1773, Train Accuracy: 0.9394, Val Loss: 0.1696, Val Accuracy: 0.9410
Epoch 386/1000, Train Loss: 0.1743, Train Accuracy: 0.9400, Val Loss: 0.1691, Val Accuracy: 0.9410
Epoch 387/1000, Train Loss: 0.1750, Train Accuracy: 0.9412, Val Loss: 0.1686, Val Accuracy: 0.9410
Epoch 388/1000, Train Loss: 0.1741, Train Accuracy: 0.9392, Val Loss: 0.1682, Val Accuracy: 0.9413
Epoch 389/1000, Train Loss: 0.1724, Train Accuracy: 0.9415, Val Loss: 0.1677, Val Accuracy: 0.9415
Epoch 390/1000, Train Loss: 0.1743, Train Accuracy: 0.9397, Val Loss: 0.1672, Val Accuracy: 0.9415
Epoch 391/1000, Train Loss: 0.1739, Train Accuracy: 0.9399, Val Loss: 0.1668, Val Accuracy: 0.9420
Epoch 392/1000, Train Loss: 0.1734, Train Accuracy: 0.9402, Val Loss: 0.1664, Val Accuracy: 0.9420
Epoch 393/1000, Train Loss: 0.1731, Train Accuracy: 0.9400, Val Loss: 0.1659, Val Accuracy: 0.9423
Epoch 394/1000, Train Loss: 0.1715, Train Accuracy: 0.9400, Val Loss: 0.1654, Val Accuracy: 0.9429
Epoch 395/1000, Train Loss: 0.1702, Train Accuracy: 0.9402, Val Loss: 0.1650, Val Accuracy: 0.9429
Epoch 396/1000, Train Loss: 0.1702, Train Accuracy: 0.9410, Val Loss: 0.1645, Val Accuracy: 0.9432
Epoch 397/1000, Train Loss: 0.1708, Train Accuracy: 0.9410, Val Loss: 0.1641, Val Accuracy: 0.9432
Epoch 398/1000, Train Loss: 0.1702, Train Accuracy: 0.9416, Val Loss: 0.1637, Val Accuracy: 0.9435
Epoch 399/1000, Train Loss: 0.1696, Train Accuracy: 0.9427, Val Loss: 0.1633, Val Accuracy: 0.9437
Epoch 400/1000, Train Loss: 0.1676, Train Accuracy: 0.9416, Val Loss: 0.1628, Val Accuracy: 0.9437
Epoch 401/1000, Train Loss: 0.1675, Train Accuracy: 0.9419, Val Loss: 0.1625, Val Accuracy: 0.9434
Epoch 402/1000, Train Loss: 0.1689, Train Accuracy: 0.9408, Val Loss: 0.1620, Val Accuracy: 0.9435
Epoch 403/1000, Train Loss: 0.1666, Train Accuracy: 0.9429, Val Loss: 0.1617, Val Accuracy: 0.9437
Epoch 404/1000, Train Loss: 0.1686, Train Accuracy: 0.9397, Val Loss: 0.1613, Val Accuracy: 0.9437
Epoch 405/1000, Train Loss: 0.1661, Train Accuracy: 0.9428, Val Loss: 0.1610, Val Accuracy: 0.9439
Epoch 406/1000, Train Loss: 0.1662, Train Accuracy: 0.9435, Val Loss: 0.1605, Val Accuracy: 0.9442
Epoch 407/1000, Train Loss: 0.1653, Train Accuracy: 0.9430, Val Loss: 0.1601, Val Accuracy: 0.9442
Epoch 408/1000, Train Loss: 0.1638, Train Accuracy: 0.9425, Val Loss: 0.1597, Val Accuracy: 0.9444
Epoch 409/1000, Train Loss: 0.1646, Train Accuracy: 0.9428, Val Loss: 0.1593, Val Accuracy: 0.9444
Epoch 410/1000, Train Loss: 0.1642, Train Accuracy: 0.9429, Val Loss: 0.1589, Val Accuracy: 0.9446
Epoch 411/1000, Train Loss: 0.1642, Train Accuracy: 0.9433, Val Loss: 0.1586, Val Accuracy: 0.9446
Epoch 412/1000, Train Loss: 0.1633, Train Accuracy: 0.9431, Val Loss: 0.1582, Val Accuracy: 0.9444
Epoch 413/1000, Train Loss: 0.1646, Train Accuracy: 0.9427, Val Loss: 0.1579, Val Accuracy: 0.9446
Epoch 414/1000, Train Loss: 0.1621, Train Accuracy: 0.9427, Val Loss: 0.1576, Val Accuracy: 0.9446
Epoch 415/1000, Train Loss: 0.1628, Train Accuracy: 0.9433, Val Loss: 0.1572, Val Accuracy: 0.9449
Epoch 416/1000, Train Loss: 0.1623, Train Accuracy: 0.9442, Val Loss: 0.1569, Val Accuracy: 0.9449
Epoch 417/1000, Train Loss: 0.1613, Train Accuracy: 0.9450, Val Loss: 0.1565, Val Accuracy: 0.9451
Epoch 418/1000, Train Loss: 0.1615, Train Accuracy: 0.9451, Val Loss: 0.1562, Val Accuracy: 0.9453
Epoch 419/1000, Train Loss: 0.1613, Train Accuracy: 0.9438, Val Loss: 0.1558, Val Accuracy: 0.9453
Epoch 420/1000, Train Loss: 0.1604, Train Accuracy: 0.9458, Val Loss: 0.1554, Val Accuracy: 0.9456
Epoch 421/1000, Train Loss: 0.1602, Train Accuracy: 0.9446, Val Loss: 0.1550, Val Accuracy: 0.9460
Epoch 422/1000, Train Loss: 0.1598, Train Accuracy: 0.9456, Val Loss: 0.1547, Val Accuracy: 0.9460
Epoch 423/1000, Train Loss: 0.1586, Train Accuracy: 0.9434, Val Loss: 0.1544, Val Accuracy: 0.9460
Epoch 424/1000, Train Loss: 0.1568, Train Accuracy: 0.9463, Val Loss: 0.1540, Val Accuracy: 0.9461
Epoch 425/1000, Train Loss: 0.1576, Train Accuracy: 0.9444, Val Loss: 0.1536, Val Accuracy: 0.9463
Epoch 426/1000, Train Loss: 0.1575, Train Accuracy: 0.9453, Val Loss: 0.1533, Val Accuracy: 0.9463
Epoch 427/1000, Train Loss: 0.1582, Train Accuracy: 0.9456, Val Loss: 0.1530, Val Accuracy: 0.9463
Epoch 428/1000, Train Loss: 0.1574, Train Accuracy: 0.9458, Val Loss: 0.1528, Val Accuracy: 0.9465
Epoch 429/1000, Train Loss: 0.1563, Train Accuracy: 0.9474, Val Loss: 0.1524, Val Accuracy: 0.9463
Epoch 430/1000, Train Loss: 0.1560, Train Accuracy: 0.9465, Val Loss: 0.1521, Val Accuracy: 0.9463
Epoch 431/1000, Train Loss: 0.1558, Train Accuracy: 0.9461, Val Loss: 0.1517, Val Accuracy: 0.9463
Epoch 432/1000, Train Loss: 0.1573, Train Accuracy: 0.9451, Val Loss: 0.1515, Val Accuracy: 0.9465
Epoch 433/1000, Train Loss: 0.1536, Train Accuracy: 0.9472, Val Loss: 0.1512, Val Accuracy: 0.9466
Epoch 434/1000, Train Loss: 0.1547, Train Accuracy: 0.9469, Val Loss: 0.1509, Val Accuracy: 0.9465
Epoch 435/1000, Train Loss: 0.1543, Train Accuracy: 0.9469, Val Loss: 0.1506, Val Accuracy: 0.9465
Epoch 436/1000, Train Loss: 0.1534, Train Accuracy: 0.9472, Val Loss: 0.1503, Val Accuracy: 0.9465
Epoch 437/1000, Train Loss: 0.1547, Train Accuracy: 0.9448, Val Loss: 0.1499, Val Accuracy: 0.9465
Epoch 438/1000, Train Loss: 0.1530, Train Accuracy: 0.9469, Val Loss: 0.1495, Val Accuracy: 0.9465
Epoch 439/1000, Train Loss: 0.1528, Train Accuracy: 0.9464, Val Loss: 0.1493, Val Accuracy: 0.9463
Epoch 440/1000, Train Loss: 0.1525, Train Accuracy: 0.9467, Val Loss: 0.1490, Val Accuracy: 0.9463
Epoch 441/1000, Train Loss: 0.1510, Train Accuracy: 0.9477, Val Loss: 0.1486, Val Accuracy: 0.9463
Epoch 442/1000, Train Loss: 0.1532, Train Accuracy: 0.9472, Val Loss: 0.1483, Val Accuracy: 0.9463
Epoch 443/1000, Train Loss: 0.1516, Train Accuracy: 0.9475, Val Loss: 0.1480, Val Accuracy: 0.9461
Epoch 444/1000, Train Loss: 0.1505, Train Accuracy: 0.9468, Val Loss: 0.1477, Val Accuracy: 0.9463
Epoch 445/1000, Train Loss: 0.1506, Train Accuracy: 0.9489, Val Loss: 0.1475, Val Accuracy: 0.9463
Epoch 446/1000, Train Loss: 0.1509, Train Accuracy: 0.9475, Val Loss: 0.1472, Val Accuracy: 0.9463
Epoch 447/1000, Train Loss: 0.1508, Train Accuracy: 0.9476, Val Loss: 0.1469, Val Accuracy: 0.9466
Epoch 448/1000, Train Loss: 0.1497, Train Accuracy: 0.9477, Val Loss: 0.1466, Val Accuracy: 0.9468
Epoch 449/1000, Train Loss: 0.1503, Train Accuracy: 0.9482, Val Loss: 0.1463, Val Accuracy: 0.9468
Epoch 450/1000, Train Loss: 0.1494, Train Accuracy: 0.9488, Val Loss: 0.1460, Val Accuracy: 0.9468
Epoch 451/1000, Train Loss: 0.1475, Train Accuracy: 0.9494, Val Loss: 0.1457, Val Accuracy: 0.9470
Epoch 452/1000, Train Loss: 0.1491, Train Accuracy: 0.9475, Val Loss: 0.1453, Val Accuracy: 0.9478
Epoch 453/1000, Train Loss: 0.1497, Train Accuracy: 0.9480, Val Loss: 0.1451, Val Accuracy: 0.9473
Epoch 454/1000, Train Loss: 0.1489, Train Accuracy: 0.9481, Val Loss: 0.1448, Val Accuracy: 0.9473
Epoch 455/1000, Train Loss: 0.1469, Train Accuracy: 0.9484, Val Loss: 0.1445, Val Accuracy: 0.9477
Epoch 456/1000, Train Loss: 0.1478, Train Accuracy: 0.9485, Val Loss: 0.1442, Val Accuracy: 0.9478
Epoch 457/1000, Train Loss: 0.1485, Train Accuracy: 0.9484, Val Loss: 0.1440, Val Accuracy: 0.9478
Epoch 458/1000, Train Loss: 0.1466, Train Accuracy: 0.9502, Val Loss: 0.1438, Val Accuracy: 0.9477
Epoch 459/1000, Train Loss: 0.1452, Train Accuracy: 0.9495, Val Loss: 0.1435, Val Accuracy: 0.9478
Epoch 460/1000, Train Loss: 0.1469, Train Accuracy: 0.9486, Val Loss: 0.1434, Val Accuracy: 0.9477
Epoch 461/1000, Train Loss: 0.1469, Train Accuracy: 0.9478, Val Loss: 0.1431, Val Accuracy: 0.9478
Epoch 462/1000, Train Loss: 0.1456, Train Accuracy: 0.9490, Val Loss: 0.1428, Val Accuracy: 0.9477
Epoch 463/1000, Train Loss: 0.1441, Train Accuracy: 0.9499, Val Loss: 0.1425, Val Accuracy: 0.9477
Epoch 464/1000, Train Loss: 0.1450, Train Accuracy: 0.9489, Val Loss: 0.1423, Val Accuracy: 0.9478
Epoch 465/1000, Train Loss: 0.1436, Train Accuracy: 0.9497, Val Loss: 0.1421, Val Accuracy: 0.9478
Epoch 466/1000, Train Loss: 0.1453, Train Accuracy: 0.9484, Val Loss: 0.1418, Val Accuracy: 0.9482
Epoch 467/1000, Train Loss: 0.1427, Train Accuracy: 0.9496, Val Loss: 0.1415, Val Accuracy: 0.9485
Epoch 468/1000, Train Loss: 0.1428, Train Accuracy: 0.9494, Val Loss: 0.1413, Val Accuracy: 0.9485
Epoch 469/1000, Train Loss: 0.1447, Train Accuracy: 0.9480, Val Loss: 0.1410, Val Accuracy: 0.9485
Epoch 470/1000, Train Loss: 0.1423, Train Accuracy: 0.9512, Val Loss: 0.1407, Val Accuracy: 0.9487
Epoch 471/1000, Train Loss: 0.1439, Train Accuracy: 0.9503, Val Loss: 0.1406, Val Accuracy: 0.9485
Epoch 472/1000, Train Loss: 0.1422, Train Accuracy: 0.9494, Val Loss: 0.1403, Val Accuracy: 0.9489
Epoch 473/1000, Train Loss: 0.1404, Train Accuracy: 0.9509, Val Loss: 0.1400, Val Accuracy: 0.9489
Epoch 474/1000, Train Loss: 0.1423, Train Accuracy: 0.9510, Val Loss: 0.1397, Val Accuracy: 0.9487
Epoch 475/1000, Train Loss: 0.1421, Train Accuracy: 0.9502, Val Loss: 0.1395, Val Accuracy: 0.9491
Epoch 476/1000, Train Loss: 0.1411, Train Accuracy: 0.9491, Val Loss: 0.1393, Val Accuracy: 0.9491
Epoch 477/1000, Train Loss: 0.1402, Train Accuracy: 0.9515, Val Loss: 0.1390, Val Accuracy: 0.9492
Epoch 478/1000, Train Loss: 0.1402, Train Accuracy: 0.9514, Val Loss: 0.1387, Val Accuracy: 0.9494
Epoch 479/1000, Train Loss: 0.1387, Train Accuracy: 0.9511, Val Loss: 0.1385, Val Accuracy: 0.9496
Epoch 480/1000, Train Loss: 0.1401, Train Accuracy: 0.9507, Val Loss: 0.1382, Val Accuracy: 0.9492
Epoch 481/1000, Train Loss: 0.1389, Train Accuracy: 0.9500, Val Loss: 0.1380, Val Accuracy: 0.9494
Epoch 482/1000, Train Loss: 0.1392, Train Accuracy: 0.9523, Val Loss: 0.1379, Val Accuracy: 0.9497
Epoch 483/1000, Train Loss: 0.1394, Train Accuracy: 0.9518, Val Loss: 0.1377, Val Accuracy: 0.9499
Epoch 484/1000, Train Loss: 0.1398, Train Accuracy: 0.9506, Val Loss: 0.1376, Val Accuracy: 0.9499
Epoch 485/1000, Train Loss: 0.1369, Train Accuracy: 0.9518, Val Loss: 0.1373, Val Accuracy: 0.9497
Epoch 486/1000, Train Loss: 0.1383, Train Accuracy: 0.9506, Val Loss: 0.1371, Val Accuracy: 0.9497
Epoch 487/1000, Train Loss: 0.1378, Train Accuracy: 0.9526, Val Loss: 0.1369, Val Accuracy: 0.9499
Epoch 488/1000, Train Loss: 0.1365, Train Accuracy: 0.9519, Val Loss: 0.1367, Val Accuracy: 0.9497
Epoch 489/1000, Train Loss: 0.1374, Train Accuracy: 0.9520, Val Loss: 0.1365, Val Accuracy: 0.9496
Epoch 490/1000, Train Loss: 0.1359, Train Accuracy: 0.9523, Val Loss: 0.1361, Val Accuracy: 0.9501
Epoch 491/1000, Train Loss: 0.1373, Train Accuracy: 0.9513, Val Loss: 0.1360, Val Accuracy: 0.9499
Epoch 492/1000, Train Loss: 0.1360, Train Accuracy: 0.9515, Val Loss: 0.1357, Val Accuracy: 0.9503
Epoch 493/1000, Train Loss: 0.1353, Train Accuracy: 0.9523, Val Loss: 0.1355, Val Accuracy: 0.9503
Epoch 494/1000, Train Loss: 0.1348, Train Accuracy: 0.9525, Val Loss: 0.1353, Val Accuracy: 0.9503
Epoch 495/1000, Train Loss: 0.1359, Train Accuracy: 0.9530, Val Loss: 0.1351, Val Accuracy: 0.9503
Epoch 496/1000, Train Loss: 0.1357, Train Accuracy: 0.9513, Val Loss: 0.1348, Val Accuracy: 0.9503
Epoch 497/1000, Train Loss: 0.1344, Train Accuracy: 0.9532, Val Loss: 0.1347, Val Accuracy: 0.9503
Epoch 498/1000, Train Loss: 0.1352, Train Accuracy: 0.9524, Val Loss: 0.1345, Val Accuracy: 0.9503
Epoch 499/1000, Train Loss: 0.1327, Train Accuracy: 0.9541, Val Loss: 0.1340, Val Accuracy: 0.9511
Epoch 500/1000, Train Loss: 0.1323, Train Accuracy: 0.9542, Val Loss: 0.1340, Val Accuracy: 0.9508
Epoch 501/1000, Train Loss: 0.1335, Train Accuracy: 0.9533, Val Loss: 0.1337, Val Accuracy: 0.9511
Epoch 502/1000, Train Loss: 0.1335, Train Accuracy: 0.9529, Val Loss: 0.1335, Val Accuracy: 0.9515
Epoch 503/1000, Train Loss: 0.1326, Train Accuracy: 0.9534, Val Loss: 0.1334, Val Accuracy: 0.9513
Epoch 504/1000, Train Loss: 0.1330, Train Accuracy: 0.9524, Val Loss: 0.1332, Val Accuracy: 0.9513
Epoch 505/1000, Train Loss: 0.1323, Train Accuracy: 0.9530, Val Loss: 0.1330, Val Accuracy: 0.9515
Epoch 506/1000, Train Loss: 0.1328, Train Accuracy: 0.9538, Val Loss: 0.1328, Val Accuracy: 0.9515
Epoch 507/1000, Train Loss: 0.1324, Train Accuracy: 0.9540, Val Loss: 0.1326, Val Accuracy: 0.9511
Epoch 508/1000, Train Loss: 0.1306, Train Accuracy: 0.9524, Val Loss: 0.1325, Val Accuracy: 0.9515
Epoch 509/1000, Train Loss: 0.1299, Train Accuracy: 0.9548, Val Loss: 0.1322, Val Accuracy: 0.9515
Epoch 510/1000, Train Loss: 0.1311, Train Accuracy: 0.9542, Val Loss: 0.1319, Val Accuracy: 0.9515
Epoch 511/1000, Train Loss: 0.1302, Train Accuracy: 0.9559, Val Loss: 0.1318, Val Accuracy: 0.9515
Epoch 512/1000, Train Loss: 0.1286, Train Accuracy: 0.9545, Val Loss: 0.1315, Val Accuracy: 0.9516
Epoch 513/1000, Train Loss: 0.1319, Train Accuracy: 0.9527, Val Loss: 0.1314, Val Accuracy: 0.9516
Epoch 514/1000, Train Loss: 0.1290, Train Accuracy: 0.9542, Val Loss: 0.1312, Val Accuracy: 0.9518
Epoch 515/1000, Train Loss: 0.1310, Train Accuracy: 0.9542, Val Loss: 0.1309, Val Accuracy: 0.9518
Epoch 516/1000, Train Loss: 0.1299, Train Accuracy: 0.9528, Val Loss: 0.1308, Val Accuracy: 0.9520
Epoch 517/1000, Train Loss: 0.1305, Train Accuracy: 0.9527, Val Loss: 0.1307, Val Accuracy: 0.9522
Epoch 518/1000, Train Loss: 0.1302, Train Accuracy: 0.9527, Val Loss: 0.1304, Val Accuracy: 0.9522
Epoch 519/1000, Train Loss: 0.1280, Train Accuracy: 0.9561, Val Loss: 0.1303, Val Accuracy: 0.9520
Epoch 520/1000, Train Loss: 0.1270, Train Accuracy: 0.9563, Val Loss: 0.1301, Val Accuracy: 0.9523
Epoch 521/1000, Train Loss: 0.1278, Train Accuracy: 0.9551, Val Loss: 0.1299, Val Accuracy: 0.9525
Epoch 522/1000, Train Loss: 0.1284, Train Accuracy: 0.9551, Val Loss: 0.1297, Val Accuracy: 0.9522
Epoch 523/1000, Train Loss: 0.1269, Train Accuracy: 0.9561, Val Loss: 0.1296, Val Accuracy: 0.9525
Epoch 524/1000, Train Loss: 0.1274, Train Accuracy: 0.9559, Val Loss: 0.1295, Val Accuracy: 0.9525
Epoch 525/1000, Train Loss: 0.1267, Train Accuracy: 0.9562, Val Loss: 0.1292, Val Accuracy: 0.9525
Epoch 526/1000, Train Loss: 0.1283, Train Accuracy: 0.9544, Val Loss: 0.1291, Val Accuracy: 0.9525
Epoch 527/1000, Train Loss: 0.1250, Train Accuracy: 0.9556, Val Loss: 0.1289, Val Accuracy: 0.9523
Epoch 528/1000, Train Loss: 0.1279, Train Accuracy: 0.9555, Val Loss: 0.1288, Val Accuracy: 0.9523
Epoch 529/1000, Train Loss: 0.1273, Train Accuracy: 0.9564, Val Loss: 0.1285, Val Accuracy: 0.9523
Epoch 530/1000, Train Loss: 0.1247, Train Accuracy: 0.9570, Val Loss: 0.1283, Val Accuracy: 0.9522
Epoch 531/1000, Train Loss: 0.1247, Train Accuracy: 0.9567, Val Loss: 0.1281, Val Accuracy: 0.9522
Epoch 532/1000, Train Loss: 0.1247, Train Accuracy: 0.9568, Val Loss: 0.1280, Val Accuracy: 0.9520
Epoch 533/1000, Train Loss: 0.1258, Train Accuracy: 0.9552, Val Loss: 0.1279, Val Accuracy: 0.9522
Epoch 534/1000, Train Loss: 0.1251, Train Accuracy: 0.9574, Val Loss: 0.1276, Val Accuracy: 0.9523
Epoch 535/1000, Train Loss: 0.1237, Train Accuracy: 0.9568, Val Loss: 0.1275, Val Accuracy: 0.9527
Epoch 536/1000, Train Loss: 0.1252, Train Accuracy: 0.9570, Val Loss: 0.1272, Val Accuracy: 0.9527
Epoch 537/1000, Train Loss: 0.1242, Train Accuracy: 0.9567, Val Loss: 0.1270, Val Accuracy: 0.9530
Epoch 538/1000, Train Loss: 0.1227, Train Accuracy: 0.9569, Val Loss: 0.1269, Val Accuracy: 0.9527
Epoch 539/1000, Train Loss: 0.1244, Train Accuracy: 0.9571, Val Loss: 0.1267, Val Accuracy: 0.9530
Epoch 540/1000, Train Loss: 0.1239, Train Accuracy: 0.9562, Val Loss: 0.1267, Val Accuracy: 0.9530
Epoch 541/1000, Train Loss: 0.1224, Train Accuracy: 0.9560, Val Loss: 0.1264, Val Accuracy: 0.9527
Epoch 542/1000, Train Loss: 0.1242, Train Accuracy: 0.9560, Val Loss: 0.1262, Val Accuracy: 0.9528
Epoch 543/1000, Train Loss: 0.1245, Train Accuracy: 0.9564, Val Loss: 0.1261, Val Accuracy: 0.9527
Epoch 544/1000, Train Loss: 0.1227, Train Accuracy: 0.9580, Val Loss: 0.1260, Val Accuracy: 0.9527
Epoch 545/1000, Train Loss: 0.1232, Train Accuracy: 0.9564, Val Loss: 0.1258, Val Accuracy: 0.9528
Epoch 546/1000, Train Loss: 0.1228, Train Accuracy: 0.9570, Val Loss: 0.1257, Val Accuracy: 0.9534
Epoch 547/1000, Train Loss: 0.1210, Train Accuracy: 0.9582, Val Loss: 0.1256, Val Accuracy: 0.9532
Epoch 548/1000, Train Loss: 0.1214, Train Accuracy: 0.9572, Val Loss: 0.1254, Val Accuracy: 0.9534
Epoch 549/1000, Train Loss: 0.1210, Train Accuracy: 0.9570, Val Loss: 0.1251, Val Accuracy: 0.9532
Epoch 550/1000, Train Loss: 0.1217, Train Accuracy: 0.9586, Val Loss: 0.1252, Val Accuracy: 0.9537
Epoch 551/1000, Train Loss: 0.1223, Train Accuracy: 0.9569, Val Loss: 0.1248, Val Accuracy: 0.9539
Epoch 552/1000, Train Loss: 0.1206, Train Accuracy: 0.9570, Val Loss: 0.1249, Val Accuracy: 0.9535
Epoch 553/1000, Train Loss: 0.1211, Train Accuracy: 0.9570, Val Loss: 0.1246, Val Accuracy: 0.9539
Epoch 554/1000, Train Loss: 0.1214, Train Accuracy: 0.9577, Val Loss: 0.1245, Val Accuracy: 0.9535
Epoch 555/1000, Train Loss: 0.1205, Train Accuracy: 0.9577, Val Loss: 0.1242, Val Accuracy: 0.9539
Epoch 556/1000, Train Loss: 0.1184, Train Accuracy: 0.9588, Val Loss: 0.1241, Val Accuracy: 0.9537
Epoch 557/1000, Train Loss: 0.1204, Train Accuracy: 0.9570, Val Loss: 0.1239, Val Accuracy: 0.9539
Epoch 558/1000, Train Loss: 0.1200, Train Accuracy: 0.9583, Val Loss: 0.1238, Val Accuracy: 0.9540
Epoch 559/1000, Train Loss: 0.1186, Train Accuracy: 0.9585, Val Loss: 0.1237, Val Accuracy: 0.9540
Epoch 560/1000, Train Loss: 0.1189, Train Accuracy: 0.9577, Val Loss: 0.1237, Val Accuracy: 0.9539
Epoch 561/1000, Train Loss: 0.1184, Train Accuracy: 0.9586, Val Loss: 0.1235, Val Accuracy: 0.9539
Epoch 562/1000, Train Loss: 0.1189, Train Accuracy: 0.9584, Val Loss: 0.1233, Val Accuracy: 0.9542
Epoch 563/1000, Train Loss: 0.1204, Train Accuracy: 0.9582, Val Loss: 0.1232, Val Accuracy: 0.9542
Epoch 564/1000, Train Loss: 0.1165, Train Accuracy: 0.9591, Val Loss: 0.1230, Val Accuracy: 0.9542
Epoch 565/1000, Train Loss: 0.1186, Train Accuracy: 0.9580, Val Loss: 0.1230, Val Accuracy: 0.9542
Epoch 566/1000, Train Loss: 0.1192, Train Accuracy: 0.9580, Val Loss: 0.1226, Val Accuracy: 0.9542
Epoch 567/1000, Train Loss: 0.1173, Train Accuracy: 0.9594, Val Loss: 0.1227, Val Accuracy: 0.9540
Epoch 568/1000, Train Loss: 0.1182, Train Accuracy: 0.9580, Val Loss: 0.1224, Val Accuracy: 0.9546
Epoch 569/1000, Train Loss: 0.1152, Train Accuracy: 0.9586, Val Loss: 0.1222, Val Accuracy: 0.9549
Epoch 570/1000, Train Loss: 0.1172, Train Accuracy: 0.9589, Val Loss: 0.1221, Val Accuracy: 0.9547
Epoch 571/1000, Train Loss: 0.1159, Train Accuracy: 0.9594, Val Loss: 0.1221, Val Accuracy: 0.9546
Epoch 572/1000, Train Loss: 0.1174, Train Accuracy: 0.9567, Val Loss: 0.1218, Val Accuracy: 0.9547
Epoch 573/1000, Train Loss: 0.1176, Train Accuracy: 0.9579, Val Loss: 0.1218, Val Accuracy: 0.9546
Epoch 574/1000, Train Loss: 0.1150, Train Accuracy: 0.9596, Val Loss: 0.1216, Val Accuracy: 0.9546
Epoch 575/1000, Train Loss: 0.1135, Train Accuracy: 0.9609, Val Loss: 0.1215, Val Accuracy: 0.9546
Epoch 576/1000, Train Loss: 0.1155, Train Accuracy: 0.9597, Val Loss: 0.1213, Val Accuracy: 0.9546
Epoch 577/1000, Train Loss: 0.1168, Train Accuracy: 0.9589, Val Loss: 0.1210, Val Accuracy: 0.9547
Epoch 578/1000, Train Loss: 0.1151, Train Accuracy: 0.9597, Val Loss: 0.1208, Val Accuracy: 0.9547
Epoch 579/1000, Train Loss: 0.1157, Train Accuracy: 0.9605, Val Loss: 0.1208, Val Accuracy: 0.9547
Epoch 580/1000, Train Loss: 0.1140, Train Accuracy: 0.9594, Val Loss: 0.1207, Val Accuracy: 0.9546
Epoch 581/1000, Train Loss: 0.1127, Train Accuracy: 0.9607, Val Loss: 0.1205, Val Accuracy: 0.9544
Epoch 582/1000, Train Loss: 0.1150, Train Accuracy: 0.9599, Val Loss: 0.1204, Val Accuracy: 0.9544
Epoch 583/1000, Train Loss: 0.1138, Train Accuracy: 0.9601, Val Loss: 0.1203, Val Accuracy: 0.9542
Epoch 584/1000, Train Loss: 0.1150, Train Accuracy: 0.9600, Val Loss: 0.1202, Val Accuracy: 0.9546
Epoch 585/1000, Train Loss: 0.1138, Train Accuracy: 0.9601, Val Loss: 0.1198, Val Accuracy: 0.9546
Epoch 586/1000, Train Loss: 0.1151, Train Accuracy: 0.9595, Val Loss: 0.1197, Val Accuracy: 0.9544
Epoch 587/1000, Train Loss: 0.1132, Train Accuracy: 0.9607, Val Loss: 0.1196, Val Accuracy: 0.9546
Epoch 588/1000, Train Loss: 0.1135, Train Accuracy: 0.9607, Val Loss: 0.1195, Val Accuracy: 0.9546
Epoch 589/1000, Train Loss: 0.1138, Train Accuracy: 0.9597, Val Loss: 0.1192, Val Accuracy: 0.9547
Epoch 590/1000, Train Loss: 0.1127, Train Accuracy: 0.9617, Val Loss: 0.1190, Val Accuracy: 0.9547
Epoch 591/1000, Train Loss: 0.1116, Train Accuracy: 0.9606, Val Loss: 0.1189, Val Accuracy: 0.9547
Epoch 592/1000, Train Loss: 0.1132, Train Accuracy: 0.9595, Val Loss: 0.1188, Val Accuracy: 0.9549
Epoch 593/1000, Train Loss: 0.1110, Train Accuracy: 0.9614, Val Loss: 0.1187, Val Accuracy: 0.9549
Epoch 594/1000, Train Loss: 0.1117, Train Accuracy: 0.9609, Val Loss: 0.1186, Val Accuracy: 0.9551
Epoch 595/1000, Train Loss: 0.1116, Train Accuracy: 0.9607, Val Loss: 0.1185, Val Accuracy: 0.9551
Epoch 596/1000, Train Loss: 0.1124, Train Accuracy: 0.9601, Val Loss: 0.1185, Val Accuracy: 0.9551
Epoch 597/1000, Train Loss: 0.1106, Train Accuracy: 0.9627, Val Loss: 0.1184, Val Accuracy: 0.9554
Epoch 598/1000, Train Loss: 0.1112, Train Accuracy: 0.9611, Val Loss: 0.1183, Val Accuracy: 0.9556
Epoch 599/1000, Train Loss: 0.1131, Train Accuracy: 0.9599, Val Loss: 0.1181, Val Accuracy: 0.9561
Epoch 600/1000, Train Loss: 0.1113, Train Accuracy: 0.9611, Val Loss: 0.1181, Val Accuracy: 0.9559
Epoch 601/1000, Train Loss: 0.1116, Train Accuracy: 0.9602, Val Loss: 0.1180, Val Accuracy: 0.9559
Epoch 602/1000, Train Loss: 0.1097, Train Accuracy: 0.9620, Val Loss: 0.1180, Val Accuracy: 0.9558
Epoch 603/1000, Train Loss: 0.1109, Train Accuracy: 0.9607, Val Loss: 0.1177, Val Accuracy: 0.9554
Epoch 604/1000, Train Loss: 0.1102, Train Accuracy: 0.9622, Val Loss: 0.1175, Val Accuracy: 0.9558
Epoch 605/1000, Train Loss: 0.1101, Train Accuracy: 0.9612, Val Loss: 0.1174, Val Accuracy: 0.9559
Epoch 606/1000, Train Loss: 0.1087, Train Accuracy: 0.9621, Val Loss: 0.1172, Val Accuracy: 0.9559
Epoch 607/1000, Train Loss: 0.1093, Train Accuracy: 0.9619, Val Loss: 0.1172, Val Accuracy: 0.9559
Epoch 608/1000, Train Loss: 0.1073, Train Accuracy: 0.9638, Val Loss: 0.1171, Val Accuracy: 0.9561
Epoch 609/1000, Train Loss: 0.1084, Train Accuracy: 0.9626, Val Loss: 0.1169, Val Accuracy: 0.9559
Epoch 610/1000, Train Loss: 0.1088, Train Accuracy: 0.9626, Val Loss: 0.1169, Val Accuracy: 0.9559
Epoch 611/1000, Train Loss: 0.1080, Train Accuracy: 0.9617, Val Loss: 0.1169, Val Accuracy: 0.9561
Epoch 612/1000, Train Loss: 0.1078, Train Accuracy: 0.9619, Val Loss: 0.1167, Val Accuracy: 0.9559
Epoch 613/1000, Train Loss: 0.1067, Train Accuracy: 0.9636, Val Loss: 0.1166, Val Accuracy: 0.9558
Epoch 614/1000, Train Loss: 0.1092, Train Accuracy: 0.9625, Val Loss: 0.1163, Val Accuracy: 0.9558
Epoch 615/1000, Train Loss: 0.1081, Train Accuracy: 0.9620, Val Loss: 0.1163, Val Accuracy: 0.9561
Epoch 616/1000, Train Loss: 0.1074, Train Accuracy: 0.9627, Val Loss: 0.1163, Val Accuracy: 0.9559
Epoch 617/1000, Train Loss: 0.1081, Train Accuracy: 0.9621, Val Loss: 0.1160, Val Accuracy: 0.9561
Epoch 618/1000, Train Loss: 0.1038, Train Accuracy: 0.9646, Val Loss: 0.1157, Val Accuracy: 0.9558
Epoch 619/1000, Train Loss: 0.1086, Train Accuracy: 0.9606, Val Loss: 0.1157, Val Accuracy: 0.9559
Epoch 620/1000, Train Loss: 0.1069, Train Accuracy: 0.9633, Val Loss: 0.1157, Val Accuracy: 0.9559
Epoch 621/1000, Train Loss: 0.1067, Train Accuracy: 0.9623, Val Loss: 0.1156, Val Accuracy: 0.9558
Epoch 622/1000, Train Loss: 0.1077, Train Accuracy: 0.9626, Val Loss: 0.1155, Val Accuracy: 0.9561
Epoch 623/1000, Train Loss: 0.1062, Train Accuracy: 0.9626, Val Loss: 0.1155, Val Accuracy: 0.9559
Epoch 624/1000, Train Loss: 0.1062, Train Accuracy: 0.9627, Val Loss: 0.1151, Val Accuracy: 0.9561
Epoch 625/1000, Train Loss: 0.1066, Train Accuracy: 0.9624, Val Loss: 0.1150, Val Accuracy: 0.9563
Epoch 626/1000, Train Loss: 0.1056, Train Accuracy: 0.9632, Val Loss: 0.1148, Val Accuracy: 0.9561
Epoch 627/1000, Train Loss: 0.1076, Train Accuracy: 0.9616, Val Loss: 0.1147, Val Accuracy: 0.9561
Epoch 628/1000, Train Loss: 0.1046, Train Accuracy: 0.9646, Val Loss: 0.1145, Val Accuracy: 0.9563
Epoch 629/1000, Train Loss: 0.1057, Train Accuracy: 0.9629, Val Loss: 0.1147, Val Accuracy: 0.9563
Epoch 630/1000, Train Loss: 0.1065, Train Accuracy: 0.9617, Val Loss: 0.1145, Val Accuracy: 0.9563
Epoch 631/1000, Train Loss: 0.1042, Train Accuracy: 0.9629, Val Loss: 0.1143, Val Accuracy: 0.9561
Epoch 632/1000, Train Loss: 0.1047, Train Accuracy: 0.9631, Val Loss: 0.1144, Val Accuracy: 0.9565
Epoch 633/1000, Train Loss: 0.1060, Train Accuracy: 0.9625, Val Loss: 0.1143, Val Accuracy: 0.9566
Epoch 634/1000, Train Loss: 0.1042, Train Accuracy: 0.9632, Val Loss: 0.1141, Val Accuracy: 0.9566
Epoch 635/1000, Train Loss: 0.1042, Train Accuracy: 0.9645, Val Loss: 0.1138, Val Accuracy: 0.9565
Epoch 636/1000, Train Loss: 0.1036, Train Accuracy: 0.9634, Val Loss: 0.1139, Val Accuracy: 0.9566
Epoch 637/1000, Train Loss: 0.1025, Train Accuracy: 0.9641, Val Loss: 0.1138, Val Accuracy: 0.9566
Epoch 638/1000, Train Loss: 0.1048, Train Accuracy: 0.9632, Val Loss: 0.1136, Val Accuracy: 0.9570
Epoch 639/1000, Train Loss: 0.1043, Train Accuracy: 0.9629, Val Loss: 0.1134, Val Accuracy: 0.9568
Epoch 640/1000, Train Loss: 0.1024, Train Accuracy: 0.9647, Val Loss: 0.1133, Val Accuracy: 0.9568
Epoch 641/1000, Train Loss: 0.1034, Train Accuracy: 0.9642, Val Loss: 0.1131, Val Accuracy: 0.9566
Epoch 642/1000, Train Loss: 0.1018, Train Accuracy: 0.9649, Val Loss: 0.1130, Val Accuracy: 0.9566
Epoch 643/1000, Train Loss: 0.1015, Train Accuracy: 0.9641, Val Loss: 0.1130, Val Accuracy: 0.9566
Epoch 644/1000, Train Loss: 0.1020, Train Accuracy: 0.9643, Val Loss: 0.1132, Val Accuracy: 0.9563
Epoch 645/1000, Train Loss: 0.1034, Train Accuracy: 0.9641, Val Loss: 0.1130, Val Accuracy: 0.9563
Epoch 646/1000, Train Loss: 0.1021, Train Accuracy: 0.9657, Val Loss: 0.1127, Val Accuracy: 0.9565
Epoch 647/1000, Train Loss: 0.1011, Train Accuracy: 0.9647, Val Loss: 0.1129, Val Accuracy: 0.9563
Epoch 648/1000, Train Loss: 0.1010, Train Accuracy: 0.9645, Val Loss: 0.1126, Val Accuracy: 0.9566
Epoch 649/1000, Train Loss: 0.1024, Train Accuracy: 0.9650, Val Loss: 0.1123, Val Accuracy: 0.9566
Epoch 650/1000, Train Loss: 0.1035, Train Accuracy: 0.9626, Val Loss: 0.1122, Val Accuracy: 0.9568
Epoch 651/1000, Train Loss: 0.1014, Train Accuracy: 0.9640, Val Loss: 0.1122, Val Accuracy: 0.9568
Epoch 652/1000, Train Loss: 0.1005, Train Accuracy: 0.9648, Val Loss: 0.1122, Val Accuracy: 0.9561
Epoch 653/1000, Train Loss: 0.1016, Train Accuracy: 0.9631, Val Loss: 0.1119, Val Accuracy: 0.9566
Epoch 654/1000, Train Loss: 0.0999, Train Accuracy: 0.9647, Val Loss: 0.1118, Val Accuracy: 0.9568
Epoch 655/1000, Train Loss: 0.1025, Train Accuracy: 0.9649, Val Loss: 0.1117, Val Accuracy: 0.9568
Epoch 656/1000, Train Loss: 0.1007, Train Accuracy: 0.9641, Val Loss: 0.1116, Val Accuracy: 0.9566
Epoch 657/1000, Train Loss: 0.1014, Train Accuracy: 0.9636, Val Loss: 0.1116, Val Accuracy: 0.9565
Epoch 658/1000, Train Loss: 0.1004, Train Accuracy: 0.9651, Val Loss: 0.1114, Val Accuracy: 0.9566
Epoch 659/1000, Train Loss: 0.0984, Train Accuracy: 0.9666, Val Loss: 0.1114, Val Accuracy: 0.9565
Epoch 660/1000, Train Loss: 0.0997, Train Accuracy: 0.9632, Val Loss: 0.1113, Val Accuracy: 0.9568
Epoch 661/1000, Train Loss: 0.0993, Train Accuracy: 0.9657, Val Loss: 0.1111, Val Accuracy: 0.9568
Epoch 662/1000, Train Loss: 0.0991, Train Accuracy: 0.9662, Val Loss: 0.1109, Val Accuracy: 0.9573
Epoch 663/1000, Train Loss: 0.0998, Train Accuracy: 0.9654, Val Loss: 0.1110, Val Accuracy: 0.9570
Epoch 664/1000, Train Loss: 0.0993, Train Accuracy: 0.9658, Val Loss: 0.1108, Val Accuracy: 0.9570
Epoch 665/1000, Train Loss: 0.0994, Train Accuracy: 0.9640, Val Loss: 0.1106, Val Accuracy: 0.9570
Epoch 666/1000, Train Loss: 0.0973, Train Accuracy: 0.9648, Val Loss: 0.1105, Val Accuracy: 0.9570
Epoch 667/1000, Train Loss: 0.0976, Train Accuracy: 0.9663, Val Loss: 0.1104, Val Accuracy: 0.9571
Epoch 668/1000, Train Loss: 0.0991, Train Accuracy: 0.9650, Val Loss: 0.1103, Val Accuracy: 0.9570
Epoch 669/1000, Train Loss: 0.0982, Train Accuracy: 0.9654, Val Loss: 0.1103, Val Accuracy: 0.9573
Epoch 670/1000, Train Loss: 0.0976, Train Accuracy: 0.9655, Val Loss: 0.1103, Val Accuracy: 0.9571
Epoch 671/1000, Train Loss: 0.0982, Train Accuracy: 0.9651, Val Loss: 0.1100, Val Accuracy: 0.9571
Epoch 672/1000, Train Loss: 0.0977, Train Accuracy: 0.9659, Val Loss: 0.1100, Val Accuracy: 0.9570
Epoch 673/1000, Train Loss: 0.0981, Train Accuracy: 0.9657, Val Loss: 0.1099, Val Accuracy: 0.9571
Epoch 674/1000, Train Loss: 0.0995, Train Accuracy: 0.9646, Val Loss: 0.1098, Val Accuracy: 0.9573
Epoch 675/1000, Train Loss: 0.0956, Train Accuracy: 0.9676, Val Loss: 0.1097, Val Accuracy: 0.9571
Epoch 676/1000, Train Loss: 0.0975, Train Accuracy: 0.9664, Val Loss: 0.1097, Val Accuracy: 0.9570
Epoch 677/1000, Train Loss: 0.0973, Train Accuracy: 0.9656, Val Loss: 0.1096, Val Accuracy: 0.9573
Epoch 678/1000, Train Loss: 0.0987, Train Accuracy: 0.9649, Val Loss: 0.1093, Val Accuracy: 0.9575
Epoch 679/1000, Train Loss: 0.0965, Train Accuracy: 0.9665, Val Loss: 0.1093, Val Accuracy: 0.9577
Epoch 680/1000, Train Loss: 0.0974, Train Accuracy: 0.9666, Val Loss: 0.1092, Val Accuracy: 0.9575
Epoch 681/1000, Train Loss: 0.0960, Train Accuracy: 0.9680, Val Loss: 0.1091, Val Accuracy: 0.9577
Epoch 682/1000, Train Loss: 0.0963, Train Accuracy: 0.9669, Val Loss: 0.1090, Val Accuracy: 0.9577
Epoch 683/1000, Train Loss: 0.0978, Train Accuracy: 0.9667, Val Loss: 0.1090, Val Accuracy: 0.9577
Epoch 684/1000, Train Loss: 0.0962, Train Accuracy: 0.9666, Val Loss: 0.1090, Val Accuracy: 0.9577
Epoch 685/1000, Train Loss: 0.0965, Train Accuracy: 0.9666, Val Loss: 0.1087, Val Accuracy: 0.9578
Epoch 686/1000, Train Loss: 0.0949, Train Accuracy: 0.9675, Val Loss: 0.1088, Val Accuracy: 0.9582
Epoch 687/1000, Train Loss: 0.0962, Train Accuracy: 0.9661, Val Loss: 0.1087, Val Accuracy: 0.9582
Epoch 688/1000, Train Loss: 0.0949, Train Accuracy: 0.9671, Val Loss: 0.1086, Val Accuracy: 0.9582
Epoch 689/1000, Train Loss: 0.0959, Train Accuracy: 0.9661, Val Loss: 0.1085, Val Accuracy: 0.9582
Epoch 690/1000, Train Loss: 0.0949, Train Accuracy: 0.9671, Val Loss: 0.1083, Val Accuracy: 0.9583
Epoch 691/1000, Train Loss: 0.0945, Train Accuracy: 0.9669, Val Loss: 0.1082, Val Accuracy: 0.9582
Epoch 692/1000, Train Loss: 0.0938, Train Accuracy: 0.9676, Val Loss: 0.1082, Val Accuracy: 0.9583
Epoch 693/1000, Train Loss: 0.0956, Train Accuracy: 0.9681, Val Loss: 0.1081, Val Accuracy: 0.9582
Epoch 694/1000, Train Loss: 0.0941, Train Accuracy: 0.9669, Val Loss: 0.1080, Val Accuracy: 0.9583
Epoch 695/1000, Train Loss: 0.0941, Train Accuracy: 0.9664, Val Loss: 0.1080, Val Accuracy: 0.9582
Epoch 696/1000, Train Loss: 0.0935, Train Accuracy: 0.9678, Val Loss: 0.1079, Val Accuracy: 0.9580
Epoch 697/1000, Train Loss: 0.0938, Train Accuracy: 0.9674, Val Loss: 0.1079, Val Accuracy: 0.9582
Epoch 698/1000, Train Loss: 0.0946, Train Accuracy: 0.9669, Val Loss: 0.1078, Val Accuracy: 0.9585
Epoch 699/1000, Train Loss: 0.0945, Train Accuracy: 0.9675, Val Loss: 0.1075, Val Accuracy: 0.9578
Epoch 700/1000, Train Loss: 0.0952, Train Accuracy: 0.9663, Val Loss: 0.1075, Val Accuracy: 0.9580
Epoch 701/1000, Train Loss: 0.0945, Train Accuracy: 0.9671, Val Loss: 0.1074, Val Accuracy: 0.9585
Epoch 702/1000, Train Loss: 0.0926, Train Accuracy: 0.9686, Val Loss: 0.1072, Val Accuracy: 0.9582
Epoch 703/1000, Train Loss: 0.0925, Train Accuracy: 0.9680, Val Loss: 0.1071, Val Accuracy: 0.9580
Epoch 704/1000, Train Loss: 0.0926, Train Accuracy: 0.9680, Val Loss: 0.1072, Val Accuracy: 0.9589
Epoch 705/1000, Train Loss: 0.0949, Train Accuracy: 0.9676, Val Loss: 0.1072, Val Accuracy: 0.9592
Epoch 706/1000, Train Loss: 0.0918, Train Accuracy: 0.9693, Val Loss: 0.1071, Val Accuracy: 0.9589
Epoch 707/1000, Train Loss: 0.0908, Train Accuracy: 0.9692, Val Loss: 0.1070, Val Accuracy: 0.9587
Epoch 708/1000, Train Loss: 0.0921, Train Accuracy: 0.9686, Val Loss: 0.1068, Val Accuracy: 0.9585
Epoch 709/1000, Train Loss: 0.0925, Train Accuracy: 0.9686, Val Loss: 0.1067, Val Accuracy: 0.9587
Epoch 710/1000, Train Loss: 0.0932, Train Accuracy: 0.9672, Val Loss: 0.1066, Val Accuracy: 0.9587
Epoch 711/1000, Train Loss: 0.0932, Train Accuracy: 0.9684, Val Loss: 0.1067, Val Accuracy: 0.9590
Epoch 712/1000, Train Loss: 0.0925, Train Accuracy: 0.9680, Val Loss: 0.1065, Val Accuracy: 0.9590
Epoch 713/1000, Train Loss: 0.0915, Train Accuracy: 0.9691, Val Loss: 0.1065, Val Accuracy: 0.9590
Epoch 714/1000, Train Loss: 0.0901, Train Accuracy: 0.9691, Val Loss: 0.1064, Val Accuracy: 0.9590
Epoch 715/1000, Train Loss: 0.0914, Train Accuracy: 0.9683, Val Loss: 0.1063, Val Accuracy: 0.9590
Epoch 716/1000, Train Loss: 0.0918, Train Accuracy: 0.9678, Val Loss: 0.1062, Val Accuracy: 0.9592
Epoch 717/1000, Train Loss: 0.0899, Train Accuracy: 0.9685, Val Loss: 0.1060, Val Accuracy: 0.9590
Epoch 718/1000, Train Loss: 0.0899, Train Accuracy: 0.9704, Val Loss: 0.1060, Val Accuracy: 0.9594
Epoch 719/1000, Train Loss: 0.0898, Train Accuracy: 0.9684, Val Loss: 0.1058, Val Accuracy: 0.9590
Epoch 720/1000, Train Loss: 0.0896, Train Accuracy: 0.9700, Val Loss: 0.1058, Val Accuracy: 0.9592
Epoch 721/1000, Train Loss: 0.0909, Train Accuracy: 0.9694, Val Loss: 0.1058, Val Accuracy: 0.9590
Epoch 722/1000, Train Loss: 0.0896, Train Accuracy: 0.9685, Val Loss: 0.1058, Val Accuracy: 0.9594
Epoch 723/1000, Train Loss: 0.0915, Train Accuracy: 0.9690, Val Loss: 0.1055, Val Accuracy: 0.9592
Epoch 724/1000, Train Loss: 0.0908, Train Accuracy: 0.9697, Val Loss: 0.1055, Val Accuracy: 0.9596
Epoch 725/1000, Train Loss: 0.0890, Train Accuracy: 0.9695, Val Loss: 0.1054, Val Accuracy: 0.9597
Epoch 726/1000, Train Loss: 0.0896, Train Accuracy: 0.9695, Val Loss: 0.1054, Val Accuracy: 0.9597
Epoch 727/1000, Train Loss: 0.0898, Train Accuracy: 0.9681, Val Loss: 0.1052, Val Accuracy: 0.9594
Epoch 728/1000, Train Loss: 0.0891, Train Accuracy: 0.9691, Val Loss: 0.1052, Val Accuracy: 0.9596
Epoch 729/1000, Train Loss: 0.0891, Train Accuracy: 0.9692, Val Loss: 0.1052, Val Accuracy: 0.9592
Epoch 730/1000, Train Loss: 0.0878, Train Accuracy: 0.9700, Val Loss: 0.1050, Val Accuracy: 0.9594
Epoch 731/1000, Train Loss: 0.0909, Train Accuracy: 0.9688, Val Loss: 0.1049, Val Accuracy: 0.9592
Epoch 732/1000, Train Loss: 0.0886, Train Accuracy: 0.9697, Val Loss: 0.1050, Val Accuracy: 0.9596
Epoch 733/1000, Train Loss: 0.0895, Train Accuracy: 0.9691, Val Loss: 0.1049, Val Accuracy: 0.9596
Epoch 734/1000, Train Loss: 0.0888, Train Accuracy: 0.9698, Val Loss: 0.1047, Val Accuracy: 0.9594
Epoch 735/1000, Train Loss: 0.0868, Train Accuracy: 0.9692, Val Loss: 0.1047, Val Accuracy: 0.9596
Epoch 736/1000, Train Loss: 0.0892, Train Accuracy: 0.9691, Val Loss: 0.1046, Val Accuracy: 0.9596
Epoch 737/1000, Train Loss: 0.0878, Train Accuracy: 0.9706, Val Loss: 0.1047, Val Accuracy: 0.9599
Epoch 738/1000, Train Loss: 0.0876, Train Accuracy: 0.9703, Val Loss: 0.1046, Val Accuracy: 0.9597
Epoch 739/1000, Train Loss: 0.0885, Train Accuracy: 0.9698, Val Loss: 0.1045, Val Accuracy: 0.9597
Epoch 740/1000, Train Loss: 0.0873, Train Accuracy: 0.9704, Val Loss: 0.1044, Val Accuracy: 0.9596
Epoch 741/1000, Train Loss: 0.0870, Train Accuracy: 0.9707, Val Loss: 0.1044, Val Accuracy: 0.9597
Epoch 742/1000, Train Loss: 0.0872, Train Accuracy: 0.9708, Val Loss: 0.1044, Val Accuracy: 0.9597
Epoch 743/1000, Train Loss: 0.0886, Train Accuracy: 0.9694, Val Loss: 0.1043, Val Accuracy: 0.9597
Epoch 744/1000, Train Loss: 0.0854, Train Accuracy: 0.9710, Val Loss: 0.1042, Val Accuracy: 0.9597
Epoch 745/1000, Train Loss: 0.0888, Train Accuracy: 0.9692, Val Loss: 0.1041, Val Accuracy: 0.9597
Epoch 746/1000, Train Loss: 0.0862, Train Accuracy: 0.9697, Val Loss: 0.1041, Val Accuracy: 0.9596
Epoch 747/1000, Train Loss: 0.0868, Train Accuracy: 0.9696, Val Loss: 0.1040, Val Accuracy: 0.9597
Epoch 748/1000, Train Loss: 0.0850, Train Accuracy: 0.9714, Val Loss: 0.1040, Val Accuracy: 0.9597
Epoch 749/1000, Train Loss: 0.0861, Train Accuracy: 0.9704, Val Loss: 0.1039, Val Accuracy: 0.9599
Epoch 750/1000, Train Loss: 0.0868, Train Accuracy: 0.9696, Val Loss: 0.1039, Val Accuracy: 0.9597
Epoch 751/1000, Train Loss: 0.0856, Train Accuracy: 0.9706, Val Loss: 0.1038, Val Accuracy: 0.9597
Epoch 752/1000, Train Loss: 0.0860, Train Accuracy: 0.9697, Val Loss: 0.1035, Val Accuracy: 0.9597
Epoch 753/1000, Train Loss: 0.0859, Train Accuracy: 0.9710, Val Loss: 0.1035, Val Accuracy: 0.9599
Epoch 754/1000, Train Loss: 0.0873, Train Accuracy: 0.9693, Val Loss: 0.1033, Val Accuracy: 0.9597
Epoch 755/1000, Train Loss: 0.0852, Train Accuracy: 0.9719, Val Loss: 0.1032, Val Accuracy: 0.9597
Epoch 756/1000, Train Loss: 0.0834, Train Accuracy: 0.9717, Val Loss: 0.1034, Val Accuracy: 0.9597
Epoch 757/1000, Train Loss: 0.0848, Train Accuracy: 0.9703, Val Loss: 0.1032, Val Accuracy: 0.9599
Epoch 758/1000, Train Loss: 0.0852, Train Accuracy: 0.9703, Val Loss: 0.1033, Val Accuracy: 0.9599
Epoch 759/1000, Train Loss: 0.0836, Train Accuracy: 0.9714, Val Loss: 0.1031, Val Accuracy: 0.9596
Epoch 760/1000, Train Loss: 0.0858, Train Accuracy: 0.9707, Val Loss: 0.1029, Val Accuracy: 0.9599
Epoch 761/1000, Train Loss: 0.0842, Train Accuracy: 0.9710, Val Loss: 0.1028, Val Accuracy: 0.9599
Epoch 762/1000, Train Loss: 0.0848, Train Accuracy: 0.9709, Val Loss: 0.1030, Val Accuracy: 0.9599
Epoch 763/1000, Train Loss: 0.0843, Train Accuracy: 0.9713, Val Loss: 0.1030, Val Accuracy: 0.9599
Epoch 764/1000, Train Loss: 0.0850, Train Accuracy: 0.9703, Val Loss: 0.1026, Val Accuracy: 0.9601
Epoch 765/1000, Train Loss: 0.0857, Train Accuracy: 0.9709, Val Loss: 0.1027, Val Accuracy: 0.9599
Epoch 766/1000, Train Loss: 0.0835, Train Accuracy: 0.9711, Val Loss: 0.1025, Val Accuracy: 0.9599
Epoch 767/1000, Train Loss: 0.0840, Train Accuracy: 0.9707, Val Loss: 0.1024, Val Accuracy: 0.9599
Epoch 768/1000, Train Loss: 0.0844, Train Accuracy: 0.9703, Val Loss: 0.1022, Val Accuracy: 0.9602
Epoch 769/1000, Train Loss: 0.0847, Train Accuracy: 0.9702, Val Loss: 0.1025, Val Accuracy: 0.9601
Epoch 770/1000, Train Loss: 0.0825, Train Accuracy: 0.9716, Val Loss: 0.1024, Val Accuracy: 0.9601
Epoch 771/1000, Train Loss: 0.0830, Train Accuracy: 0.9722, Val Loss: 0.1023, Val Accuracy: 0.9597
Epoch 772/1000, Train Loss: 0.0824, Train Accuracy: 0.9718, Val Loss: 0.1024, Val Accuracy: 0.9602
Epoch 773/1000, Train Loss: 0.0834, Train Accuracy: 0.9723, Val Loss: 0.1022, Val Accuracy: 0.9599
Epoch 774/1000, Train Loss: 0.0821, Train Accuracy: 0.9710, Val Loss: 0.1022, Val Accuracy: 0.9597
Epoch 775/1000, Train Loss: 0.0827, Train Accuracy: 0.9725, Val Loss: 0.1020, Val Accuracy: 0.9606
Epoch 776/1000, Train Loss: 0.0833, Train Accuracy: 0.9710, Val Loss: 0.1020, Val Accuracy: 0.9604
Epoch 777/1000, Train Loss: 0.0836, Train Accuracy: 0.9709, Val Loss: 0.1020, Val Accuracy: 0.9601
Epoch 778/1000, Train Loss: 0.0829, Train Accuracy: 0.9710, Val Loss: 0.1019, Val Accuracy: 0.9601
Epoch 779/1000, Train Loss: 0.0820, Train Accuracy: 0.9724, Val Loss: 0.1018, Val Accuracy: 0.9606
Epoch 780/1000, Train Loss: 0.0828, Train Accuracy: 0.9713, Val Loss: 0.1017, Val Accuracy: 0.9606
Epoch 781/1000, Train Loss: 0.0818, Train Accuracy: 0.9713, Val Loss: 0.1018, Val Accuracy: 0.9602
Epoch 782/1000, Train Loss: 0.0818, Train Accuracy: 0.9720, Val Loss: 0.1018, Val Accuracy: 0.9602
Epoch 783/1000, Train Loss: 0.0806, Train Accuracy: 0.9726, Val Loss: 0.1016, Val Accuracy: 0.9604
Epoch 784/1000, Train Loss: 0.0814, Train Accuracy: 0.9719, Val Loss: 0.1016, Val Accuracy: 0.9606
Epoch 785/1000, Train Loss: 0.0796, Train Accuracy: 0.9731, Val Loss: 0.1016, Val Accuracy: 0.9602
Epoch 786/1000, Train Loss: 0.0822, Train Accuracy: 0.9715, Val Loss: 0.1017, Val Accuracy: 0.9606
Epoch 787/1000, Train Loss: 0.0821, Train Accuracy: 0.9719, Val Loss: 0.1014, Val Accuracy: 0.9604
Epoch 788/1000, Train Loss: 0.0821, Train Accuracy: 0.9719, Val Loss: 0.1013, Val Accuracy: 0.9604
Epoch 789/1000, Train Loss: 0.0793, Train Accuracy: 0.9741, Val Loss: 0.1012, Val Accuracy: 0.9608
Epoch 790/1000, Train Loss: 0.0820, Train Accuracy: 0.9719, Val Loss: 0.1012, Val Accuracy: 0.9604
Epoch 791/1000, Train Loss: 0.0815, Train Accuracy: 0.9729, Val Loss: 0.1010, Val Accuracy: 0.9604
Epoch 792/1000, Train Loss: 0.0814, Train Accuracy: 0.9721, Val Loss: 0.1010, Val Accuracy: 0.9608
Epoch 793/1000, Train Loss: 0.0790, Train Accuracy: 0.9730, Val Loss: 0.1010, Val Accuracy: 0.9606
Epoch 794/1000, Train Loss: 0.0817, Train Accuracy: 0.9715, Val Loss: 0.1009, Val Accuracy: 0.9604
Epoch 795/1000, Train Loss: 0.0802, Train Accuracy: 0.9725, Val Loss: 0.1011, Val Accuracy: 0.9608
Epoch 796/1000, Train Loss: 0.0817, Train Accuracy: 0.9722, Val Loss: 0.1012, Val Accuracy: 0.9608
Epoch 797/1000, Train Loss: 0.0778, Train Accuracy: 0.9738, Val Loss: 0.1009, Val Accuracy: 0.9608
Epoch 798/1000, Train Loss: 0.0795, Train Accuracy: 0.9734, Val Loss: 0.1006, Val Accuracy: 0.9608
Epoch 799/1000, Train Loss: 0.0797, Train Accuracy: 0.9726, Val Loss: 0.1008, Val Accuracy: 0.9604
Epoch 800/1000, Train Loss: 0.0779, Train Accuracy: 0.9746, Val Loss: 0.1007, Val Accuracy: 0.9608
Epoch 801/1000, Train Loss: 0.0777, Train Accuracy: 0.9736, Val Loss: 0.1003, Val Accuracy: 0.9609
Epoch 802/1000, Train Loss: 0.0796, Train Accuracy: 0.9728, Val Loss: 0.1003, Val Accuracy: 0.9606
Epoch 803/1000, Train Loss: 0.0783, Train Accuracy: 0.9734, Val Loss: 0.1002, Val Accuracy: 0.9606
Epoch 804/1000, Train Loss: 0.0779, Train Accuracy: 0.9746, Val Loss: 0.1002, Val Accuracy: 0.9608
Epoch 805/1000, Train Loss: 0.0795, Train Accuracy: 0.9720, Val Loss: 0.1003, Val Accuracy: 0.9609
Epoch 806/1000, Train Loss: 0.0776, Train Accuracy: 0.9730, Val Loss: 0.1002, Val Accuracy: 0.9609
Epoch 807/1000, Train Loss: 0.0782, Train Accuracy: 0.9736, Val Loss: 0.1002, Val Accuracy: 0.9609
Epoch 808/1000, Train Loss: 0.0782, Train Accuracy: 0.9727, Val Loss: 0.1001, Val Accuracy: 0.9609
Epoch 809/1000, Train Loss: 0.0781, Train Accuracy: 0.9736, Val Loss: 0.1001, Val Accuracy: 0.9611
Epoch 810/1000, Train Loss: 0.0795, Train Accuracy: 0.9723, Val Loss: 0.0999, Val Accuracy: 0.9611
Epoch 811/1000, Train Loss: 0.0787, Train Accuracy: 0.9734, Val Loss: 0.1001, Val Accuracy: 0.9606
Epoch 812/1000, Train Loss: 0.0795, Train Accuracy: 0.9718, Val Loss: 0.0999, Val Accuracy: 0.9611
Epoch 813/1000, Train Loss: 0.0778, Train Accuracy: 0.9734, Val Loss: 0.1000, Val Accuracy: 0.9609
Epoch 814/1000, Train Loss: 0.0773, Train Accuracy: 0.9735, Val Loss: 0.0999, Val Accuracy: 0.9620
Epoch 815/1000, Train Loss: 0.0786, Train Accuracy: 0.9730, Val Loss: 0.0997, Val Accuracy: 0.9614
Epoch 816/1000, Train Loss: 0.0775, Train Accuracy: 0.9735, Val Loss: 0.0998, Val Accuracy: 0.9614
Epoch 817/1000, Train Loss: 0.0767, Train Accuracy: 0.9738, Val Loss: 0.0997, Val Accuracy: 0.9614
Epoch 818/1000, Train Loss: 0.0782, Train Accuracy: 0.9732, Val Loss: 0.0996, Val Accuracy: 0.9613
Epoch 819/1000, Train Loss: 0.0758, Train Accuracy: 0.9746, Val Loss: 0.0996, Val Accuracy: 0.9616
Epoch 820/1000, Train Loss: 0.0765, Train Accuracy: 0.9746, Val Loss: 0.0996, Val Accuracy: 0.9618
Epoch 821/1000, Train Loss: 0.0773, Train Accuracy: 0.9744, Val Loss: 0.0996, Val Accuracy: 0.9620
Epoch 822/1000, Train Loss: 0.0737, Train Accuracy: 0.9762, Val Loss: 0.0994, Val Accuracy: 0.9618
Epoch 823/1000, Train Loss: 0.0770, Train Accuracy: 0.9730, Val Loss: 0.0993, Val Accuracy: 0.9616
Epoch 824/1000, Train Loss: 0.0764, Train Accuracy: 0.9747, Val Loss: 0.0991, Val Accuracy: 0.9616
Epoch 825/1000, Train Loss: 0.0785, Train Accuracy: 0.9728, Val Loss: 0.0994, Val Accuracy: 0.9621
Epoch 826/1000, Train Loss: 0.0767, Train Accuracy: 0.9737, Val Loss: 0.0994, Val Accuracy: 0.9621
Epoch 827/1000, Train Loss: 0.0771, Train Accuracy: 0.9742, Val Loss: 0.0993, Val Accuracy: 0.9620
Epoch 828/1000, Train Loss: 0.0745, Train Accuracy: 0.9750, Val Loss: 0.0990, Val Accuracy: 0.9616
Epoch 829/1000, Train Loss: 0.0760, Train Accuracy: 0.9742, Val Loss: 0.0992, Val Accuracy: 0.9621
Epoch 830/1000, Train Loss: 0.0757, Train Accuracy: 0.9738, Val Loss: 0.0990, Val Accuracy: 0.9620
Epoch 831/1000, Train Loss: 0.0755, Train Accuracy: 0.9743, Val Loss: 0.0988, Val Accuracy: 0.9618
Epoch 832/1000, Train Loss: 0.0756, Train Accuracy: 0.9751, Val Loss: 0.0989, Val Accuracy: 0.9620
Epoch 833/1000, Train Loss: 0.0751, Train Accuracy: 0.9748, Val Loss: 0.0986, Val Accuracy: 0.9620
Epoch 834/1000, Train Loss: 0.0749, Train Accuracy: 0.9744, Val Loss: 0.0987, Val Accuracy: 0.9620
Epoch 835/1000, Train Loss: 0.0726, Train Accuracy: 0.9756, Val Loss: 0.0988, Val Accuracy: 0.9621
Epoch 836/1000, Train Loss: 0.0759, Train Accuracy: 0.9750, Val Loss: 0.0986, Val Accuracy: 0.9620
Epoch 837/1000, Train Loss: 0.0752, Train Accuracy: 0.9746, Val Loss: 0.0985, Val Accuracy: 0.9618
Epoch 838/1000, Train Loss: 0.0737, Train Accuracy: 0.9748, Val Loss: 0.0985, Val Accuracy: 0.9614
Epoch 839/1000, Train Loss: 0.0746, Train Accuracy: 0.9744, Val Loss: 0.0985, Val Accuracy: 0.9618
Epoch 840/1000, Train Loss: 0.0757, Train Accuracy: 0.9740, Val Loss: 0.0983, Val Accuracy: 0.9620
Epoch 841/1000, Train Loss: 0.0739, Train Accuracy: 0.9753, Val Loss: 0.0982, Val Accuracy: 0.9625
Epoch 842/1000, Train Loss: 0.0753, Train Accuracy: 0.9744, Val Loss: 0.0984, Val Accuracy: 0.9621
Epoch 843/1000, Train Loss: 0.0745, Train Accuracy: 0.9744, Val Loss: 0.0983, Val Accuracy: 0.9621
Epoch 844/1000, Train Loss: 0.0733, Train Accuracy: 0.9756, Val Loss: 0.0985, Val Accuracy: 0.9623
Epoch 845/1000, Train Loss: 0.0741, Train Accuracy: 0.9741, Val Loss: 0.0978, Val Accuracy: 0.9628
Epoch 846/1000, Train Loss: 0.0740, Train Accuracy: 0.9749, Val Loss: 0.0979, Val Accuracy: 0.9627
Epoch 847/1000, Train Loss: 0.0722, Train Accuracy: 0.9751, Val Loss: 0.0979, Val Accuracy: 0.9625
Epoch 848/1000, Train Loss: 0.0742, Train Accuracy: 0.9751, Val Loss: 0.0980, Val Accuracy: 0.9623
Epoch 849/1000, Train Loss: 0.0737, Train Accuracy: 0.9749, Val Loss: 0.0980, Val Accuracy: 0.9623
Epoch 850/1000, Train Loss: 0.0732, Train Accuracy: 0.9744, Val Loss: 0.0979, Val Accuracy: 0.9625
Epoch 851/1000, Train Loss: 0.0740, Train Accuracy: 0.9748, Val Loss: 0.0982, Val Accuracy: 0.9620
Epoch 852/1000, Train Loss: 0.0719, Train Accuracy: 0.9757, Val Loss: 0.0978, Val Accuracy: 0.9623
Epoch 853/1000, Train Loss: 0.0724, Train Accuracy: 0.9752, Val Loss: 0.0977, Val Accuracy: 0.9627
Epoch 854/1000, Train Loss: 0.0726, Train Accuracy: 0.9752, Val Loss: 0.0978, Val Accuracy: 0.9623
Epoch 855/1000, Train Loss: 0.0693, Train Accuracy: 0.9774, Val Loss: 0.0974, Val Accuracy: 0.9628
Epoch 856/1000, Train Loss: 0.0727, Train Accuracy: 0.9750, Val Loss: 0.0977, Val Accuracy: 0.9630
Epoch 857/1000, Train Loss: 0.0728, Train Accuracy: 0.9752, Val Loss: 0.0976, Val Accuracy: 0.9628
Epoch 858/1000, Train Loss: 0.0731, Train Accuracy: 0.9759, Val Loss: 0.0974, Val Accuracy: 0.9628
Epoch 859/1000, Train Loss: 0.0725, Train Accuracy: 0.9754, Val Loss: 0.0974, Val Accuracy: 0.9630
Epoch 860/1000, Train Loss: 0.0711, Train Accuracy: 0.9769, Val Loss: 0.0972, Val Accuracy: 0.9632
Epoch 861/1000, Train Loss: 0.0725, Train Accuracy: 0.9755, Val Loss: 0.0973, Val Accuracy: 0.9633
Epoch 862/1000, Train Loss: 0.0702, Train Accuracy: 0.9759, Val Loss: 0.0973, Val Accuracy: 0.9632
Epoch 863/1000, Train Loss: 0.0709, Train Accuracy: 0.9759, Val Loss: 0.0972, Val Accuracy: 0.9632
Epoch 864/1000, Train Loss: 0.0703, Train Accuracy: 0.9767, Val Loss: 0.0972, Val Accuracy: 0.9630
Epoch 865/1000, Train Loss: 0.0705, Train Accuracy: 0.9765, Val Loss: 0.0973, Val Accuracy: 0.9632
Epoch 866/1000, Train Loss: 0.0723, Train Accuracy: 0.9747, Val Loss: 0.0973, Val Accuracy: 0.9633
Epoch 867/1000, Train Loss: 0.0712, Train Accuracy: 0.9756, Val Loss: 0.0969, Val Accuracy: 0.9635
Epoch 868/1000, Train Loss: 0.0691, Train Accuracy: 0.9768, Val Loss: 0.0968, Val Accuracy: 0.9635
Epoch 869/1000, Train Loss: 0.0700, Train Accuracy: 0.9770, Val Loss: 0.0973, Val Accuracy: 0.9633
Epoch 870/1000, Train Loss: 0.0718, Train Accuracy: 0.9764, Val Loss: 0.0969, Val Accuracy: 0.9637
Epoch 871/1000, Train Loss: 0.0704, Train Accuracy: 0.9767, Val Loss: 0.0965, Val Accuracy: 0.9637
Epoch 872/1000, Train Loss: 0.0721, Train Accuracy: 0.9759, Val Loss: 0.0967, Val Accuracy: 0.9633
Epoch 873/1000, Train Loss: 0.0721, Train Accuracy: 0.9762, Val Loss: 0.0967, Val Accuracy: 0.9635
Epoch 874/1000, Train Loss: 0.0718, Train Accuracy: 0.9762, Val Loss: 0.0965, Val Accuracy: 0.9633
Epoch 875/1000, Train Loss: 0.0698, Train Accuracy: 0.9765, Val Loss: 0.0970, Val Accuracy: 0.9639
Epoch 876/1000, Train Loss: 0.0688, Train Accuracy: 0.9771, Val Loss: 0.0965, Val Accuracy: 0.9633
Epoch 877/1000, Train Loss: 0.0686, Train Accuracy: 0.9781, Val Loss: 0.0967, Val Accuracy: 0.9640
Epoch 878/1000, Train Loss: 0.0714, Train Accuracy: 0.9763, Val Loss: 0.0965, Val Accuracy: 0.9637
Epoch 879/1000, Train Loss: 0.0710, Train Accuracy: 0.9759, Val Loss: 0.0965, Val Accuracy: 0.9635
Epoch 880/1000, Train Loss: 0.0685, Train Accuracy: 0.9774, Val Loss: 0.0963, Val Accuracy: 0.9637
Epoch 881/1000, Train Loss: 0.0681, Train Accuracy: 0.9769, Val Loss: 0.0964, Val Accuracy: 0.9633
Epoch 882/1000, Train Loss: 0.0686, Train Accuracy: 0.9764, Val Loss: 0.0961, Val Accuracy: 0.9637
Epoch 883/1000, Train Loss: 0.0708, Train Accuracy: 0.9762, Val Loss: 0.0961, Val Accuracy: 0.9639
Epoch 884/1000, Train Loss: 0.0702, Train Accuracy: 0.9763, Val Loss: 0.0961, Val Accuracy: 0.9635
Epoch 885/1000, Train Loss: 0.0687, Train Accuracy: 0.9759, Val Loss: 0.0964, Val Accuracy: 0.9639
Epoch 886/1000, Train Loss: 0.0710, Train Accuracy: 0.9756, Val Loss: 0.0959, Val Accuracy: 0.9642
Epoch 887/1000, Train Loss: 0.0692, Train Accuracy: 0.9769, Val Loss: 0.0962, Val Accuracy: 0.9639
Epoch 888/1000, Train Loss: 0.0668, Train Accuracy: 0.9781, Val Loss: 0.0959, Val Accuracy: 0.9639
Epoch 889/1000, Train Loss: 0.0679, Train Accuracy: 0.9772, Val Loss: 0.0961, Val Accuracy: 0.9637
Epoch 890/1000, Train Loss: 0.0674, Train Accuracy: 0.9779, Val Loss: 0.0962, Val Accuracy: 0.9640
Epoch 891/1000, Train Loss: 0.0678, Train Accuracy: 0.9767, Val Loss: 0.0961, Val Accuracy: 0.9644
Epoch 892/1000, Train Loss: 0.0685, Train Accuracy: 0.9777, Val Loss: 0.0959, Val Accuracy: 0.9640
Epoch 893/1000, Train Loss: 0.0683, Train Accuracy: 0.9771, Val Loss: 0.0958, Val Accuracy: 0.9640
Epoch 894/1000, Train Loss: 0.0690, Train Accuracy: 0.9781, Val Loss: 0.0956, Val Accuracy: 0.9640
Epoch 895/1000, Train Loss: 0.0673, Train Accuracy: 0.9781, Val Loss: 0.0956, Val Accuracy: 0.9639
Epoch 896/1000, Train Loss: 0.0695, Train Accuracy: 0.9768, Val Loss: 0.0960, Val Accuracy: 0.9642
Epoch 897/1000, Train Loss: 0.0678, Train Accuracy: 0.9766, Val Loss: 0.0957, Val Accuracy: 0.9642
Epoch 898/1000, Train Loss: 0.0668, Train Accuracy: 0.9773, Val Loss: 0.0956, Val Accuracy: 0.9642
Epoch 899/1000, Train Loss: 0.0662, Train Accuracy: 0.9782, Val Loss: 0.0958, Val Accuracy: 0.9645
Epoch 900/1000, Train Loss: 0.0662, Train Accuracy: 0.9779, Val Loss: 0.0955, Val Accuracy: 0.9642
Epoch 901/1000, Train Loss: 0.0668, Train Accuracy: 0.9769, Val Loss: 0.0953, Val Accuracy: 0.9642
Epoch 902/1000, Train Loss: 0.0686, Train Accuracy: 0.9770, Val Loss: 0.0953, Val Accuracy: 0.9642
Epoch 903/1000, Train Loss: 0.0657, Train Accuracy: 0.9784, Val Loss: 0.0956, Val Accuracy: 0.9642
Epoch 904/1000, Train Loss: 0.0674, Train Accuracy: 0.9769, Val Loss: 0.0952, Val Accuracy: 0.9645
Epoch 905/1000, Train Loss: 0.0674, Train Accuracy: 0.9780, Val Loss: 0.0950, Val Accuracy: 0.9642
Epoch 906/1000, Train Loss: 0.0650, Train Accuracy: 0.9787, Val Loss: 0.0955, Val Accuracy: 0.9644
Epoch 907/1000, Train Loss: 0.0649, Train Accuracy: 0.9792, Val Loss: 0.0953, Val Accuracy: 0.9644
Epoch 908/1000, Train Loss: 0.0671, Train Accuracy: 0.9775, Val Loss: 0.0951, Val Accuracy: 0.9644
Epoch 909/1000, Train Loss: 0.0659, Train Accuracy: 0.9782, Val Loss: 0.0950, Val Accuracy: 0.9642
Epoch 910/1000, Train Loss: 0.0671, Train Accuracy: 0.9784, Val Loss: 0.0951, Val Accuracy: 0.9644
Epoch 911/1000, Train Loss: 0.0667, Train Accuracy: 0.9782, Val Loss: 0.0951, Val Accuracy: 0.9640
Epoch 912/1000, Train Loss: 0.0653, Train Accuracy: 0.9782, Val Loss: 0.0949, Val Accuracy: 0.9640
Epoch 913/1000, Train Loss: 0.0653, Train Accuracy: 0.9781, Val Loss: 0.0952, Val Accuracy: 0.9647
Epoch 914/1000, Train Loss: 0.0654, Train Accuracy: 0.9778, Val Loss: 0.0953, Val Accuracy: 0.9645
Epoch 915/1000, Train Loss: 0.0643, Train Accuracy: 0.9790, Val Loss: 0.0952, Val Accuracy: 0.9645
Epoch 916/1000, Train Loss: 0.0645, Train Accuracy: 0.9781, Val Loss: 0.0949, Val Accuracy: 0.9645
Epoch 917/1000, Train Loss: 0.0651, Train Accuracy: 0.9796, Val Loss: 0.0950, Val Accuracy: 0.9645
Epoch 918/1000, Train Loss: 0.0652, Train Accuracy: 0.9776, Val Loss: 0.0946, Val Accuracy: 0.9644
Epoch 919/1000, Train Loss: 0.0660, Train Accuracy: 0.9781, Val Loss: 0.0947, Val Accuracy: 0.9642
Epoch 920/1000, Train Loss: 0.0648, Train Accuracy: 0.9789, Val Loss: 0.0948, Val Accuracy: 0.9644
Epoch 921/1000, Train Loss: 0.0644, Train Accuracy: 0.9776, Val Loss: 0.0947, Val Accuracy: 0.9647
Epoch 922/1000, Train Loss: 0.0636, Train Accuracy: 0.9802, Val Loss: 0.0949, Val Accuracy: 0.9647
Epoch 923/1000, Train Loss: 0.0662, Train Accuracy: 0.9775, Val Loss: 0.0944, Val Accuracy: 0.9647
Epoch 924/1000, Train Loss: 0.0651, Train Accuracy: 0.9781, Val Loss: 0.0946, Val Accuracy: 0.9649
Epoch 925/1000, Train Loss: 0.0651, Train Accuracy: 0.9787, Val Loss: 0.0945, Val Accuracy: 0.9649
Epoch 926/1000, Train Loss: 0.0660, Train Accuracy: 0.9786, Val Loss: 0.0945, Val Accuracy: 0.9647
Epoch 927/1000, Train Loss: 0.0627, Train Accuracy: 0.9790, Val Loss: 0.0943, Val Accuracy: 0.9647
Epoch 928/1000, Train Loss: 0.0628, Train Accuracy: 0.9798, Val Loss: 0.0943, Val Accuracy: 0.9644
Epoch 929/1000, Train Loss: 0.0629, Train Accuracy: 0.9789, Val Loss: 0.0943, Val Accuracy: 0.9647
Epoch 930/1000, Train Loss: 0.0637, Train Accuracy: 0.9793, Val Loss: 0.0941, Val Accuracy: 0.9645
Epoch 931/1000, Train Loss: 0.0633, Train Accuracy: 0.9788, Val Loss: 0.0940, Val Accuracy: 0.9649
Epoch 932/1000, Train Loss: 0.0649, Train Accuracy: 0.9787, Val Loss: 0.0943, Val Accuracy: 0.9647
Epoch 933/1000, Train Loss: 0.0636, Train Accuracy: 0.9788, Val Loss: 0.0940, Val Accuracy: 0.9651
Epoch 934/1000, Train Loss: 0.0631, Train Accuracy: 0.9793, Val Loss: 0.0944, Val Accuracy: 0.9649
Epoch 935/1000, Train Loss: 0.0633, Train Accuracy: 0.9795, Val Loss: 0.0941, Val Accuracy: 0.9649
Epoch 936/1000, Train Loss: 0.0634, Train Accuracy: 0.9787, Val Loss: 0.0939, Val Accuracy: 0.9645
Epoch 937/1000, Train Loss: 0.0631, Train Accuracy: 0.9787, Val Loss: 0.0943, Val Accuracy: 0.9651
Epoch 938/1000, Train Loss: 0.0633, Train Accuracy: 0.9785, Val Loss: 0.0939, Val Accuracy: 0.9649
Epoch 939/1000, Train Loss: 0.0639, Train Accuracy: 0.9788, Val Loss: 0.0939, Val Accuracy: 0.9649
Epoch 940/1000, Train Loss: 0.0624, Train Accuracy: 0.9795, Val Loss: 0.0938, Val Accuracy: 0.9651
Epoch 941/1000, Train Loss: 0.0629, Train Accuracy: 0.9793, Val Loss: 0.0945, Val Accuracy: 0.9649
Epoch 942/1000, Train Loss: 0.0620, Train Accuracy: 0.9787, Val Loss: 0.0939, Val Accuracy: 0.9652
Epoch 943/1000, Train Loss: 0.0629, Train Accuracy: 0.9795, Val Loss: 0.0939, Val Accuracy: 0.9649
Epoch 944/1000, Train Loss: 0.0626, Train Accuracy: 0.9797, Val Loss: 0.0937, Val Accuracy: 0.9651
Epoch 945/1000, Train Loss: 0.0625, Train Accuracy: 0.9790, Val Loss: 0.0937, Val Accuracy: 0.9647
Epoch 946/1000, Train Loss: 0.0639, Train Accuracy: 0.9786, Val Loss: 0.0939, Val Accuracy: 0.9649
Epoch 947/1000, Train Loss: 0.0613, Train Accuracy: 0.9807, Val Loss: 0.0936, Val Accuracy: 0.9644
Epoch 948/1000, Train Loss: 0.0626, Train Accuracy: 0.9789, Val Loss: 0.0937, Val Accuracy: 0.9651
Epoch 949/1000, Train Loss: 0.0614, Train Accuracy: 0.9799, Val Loss: 0.0938, Val Accuracy: 0.9649
Epoch 950/1000, Train Loss: 0.0609, Train Accuracy: 0.9804, Val Loss: 0.0937, Val Accuracy: 0.9651
Epoch 951/1000, Train Loss: 0.0607, Train Accuracy: 0.9807, Val Loss: 0.0934, Val Accuracy: 0.9651
Epoch 952/1000, Train Loss: 0.0611, Train Accuracy: 0.9803, Val Loss: 0.0932, Val Accuracy: 0.9645
Epoch 953/1000, Train Loss: 0.0608, Train Accuracy: 0.9802, Val Loss: 0.0934, Val Accuracy: 0.9652
Epoch 954/1000, Train Loss: 0.0606, Train Accuracy: 0.9799, Val Loss: 0.0933, Val Accuracy: 0.9651
Epoch 955/1000, Train Loss: 0.0609, Train Accuracy: 0.9804, Val Loss: 0.0931, Val Accuracy: 0.9651
Epoch 956/1000, Train Loss: 0.0609, Train Accuracy: 0.9801, Val Loss: 0.0934, Val Accuracy: 0.9652
Epoch 957/1000, Train Loss: 0.0604, Train Accuracy: 0.9798, Val Loss: 0.0933, Val Accuracy: 0.9652
Epoch 958/1000, Train Loss: 0.0614, Train Accuracy: 0.9801, Val Loss: 0.0933, Val Accuracy: 0.9652
Epoch 959/1000, Train Loss: 0.0606, Train Accuracy: 0.9793, Val Loss: 0.0929, Val Accuracy: 0.9649
Epoch 960/1000, Train Loss: 0.0599, Train Accuracy: 0.9804, Val Loss: 0.0932, Val Accuracy: 0.9652
Epoch 961/1000, Train Loss: 0.0612, Train Accuracy: 0.9797, Val Loss: 0.0932, Val Accuracy: 0.9652
Epoch 962/1000, Train Loss: 0.0607, Train Accuracy: 0.9804, Val Loss: 0.0931, Val Accuracy: 0.9651
Epoch 963/1000, Train Loss: 0.0618, Train Accuracy: 0.9793, Val Loss: 0.0927, Val Accuracy: 0.9656
Epoch 964/1000, Train Loss: 0.0597, Train Accuracy: 0.9809, Val Loss: 0.0933, Val Accuracy: 0.9656
Epoch 965/1000, Train Loss: 0.0606, Train Accuracy: 0.9797, Val Loss: 0.0930, Val Accuracy: 0.9654
Epoch 966/1000, Train Loss: 0.0583, Train Accuracy: 0.9804, Val Loss: 0.0928, Val Accuracy: 0.9652
Epoch 967/1000, Train Loss: 0.0618, Train Accuracy: 0.9790, Val Loss: 0.0930, Val Accuracy: 0.9651
Epoch 968/1000, Train Loss: 0.0590, Train Accuracy: 0.9809, Val Loss: 0.0925, Val Accuracy: 0.9647
Epoch 969/1000, Train Loss: 0.0586, Train Accuracy: 0.9813, Val Loss: 0.0927, Val Accuracy: 0.9651
Epoch 970/1000, Train Loss: 0.0608, Train Accuracy: 0.9806, Val Loss: 0.0927, Val Accuracy: 0.9649
Epoch 971/1000, Train Loss: 0.0580, Train Accuracy: 0.9803, Val Loss: 0.0931, Val Accuracy: 0.9657
Epoch 972/1000, Train Loss: 0.0591, Train Accuracy: 0.9809, Val Loss: 0.0926, Val Accuracy: 0.9654
Epoch 973/1000, Train Loss: 0.0599, Train Accuracy: 0.9802, Val Loss: 0.0933, Val Accuracy: 0.9652
Epoch 974/1000, Train Loss: 0.0594, Train Accuracy: 0.9796, Val Loss: 0.0929, Val Accuracy: 0.9659
Epoch 975/1000, Train Loss: 0.0594, Train Accuracy: 0.9805, Val Loss: 0.0926, Val Accuracy: 0.9656
Epoch 976/1000, Train Loss: 0.0595, Train Accuracy: 0.9811, Val Loss: 0.0925, Val Accuracy: 0.9659
Epoch 977/1000, Train Loss: 0.0592, Train Accuracy: 0.9811, Val Loss: 0.0923, Val Accuracy: 0.9657
Epoch 978/1000, Train Loss: 0.0596, Train Accuracy: 0.9802, Val Loss: 0.0922, Val Accuracy: 0.9654
Epoch 979/1000, Train Loss: 0.0574, Train Accuracy: 0.9812, Val Loss: 0.0928, Val Accuracy: 0.9659
Epoch 980/1000, Train Loss: 0.0578, Train Accuracy: 0.9812, Val Loss: 0.0925, Val Accuracy: 0.9657
Epoch 981/1000, Train Loss: 0.0588, Train Accuracy: 0.9808, Val Loss: 0.0924, Val Accuracy: 0.9659
Epoch 982/1000, Train Loss: 0.0585, Train Accuracy: 0.9803, Val Loss: 0.0929, Val Accuracy: 0.9656
Epoch 983/1000, Train Loss: 0.0578, Train Accuracy: 0.9813, Val Loss: 0.0922, Val Accuracy: 0.9657
Epoch 984/1000, Train Loss: 0.0577, Train Accuracy: 0.9816, Val Loss: 0.0927, Val Accuracy: 0.9657
Epoch 985/1000, Train Loss: 0.0582, Train Accuracy: 0.9808, Val Loss: 0.0923, Val Accuracy: 0.9659
Epoch 986/1000, Train Loss: 0.0593, Train Accuracy: 0.9810, Val Loss: 0.0924, Val Accuracy: 0.9657
Epoch 987/1000, Train Loss: 0.0590, Train Accuracy: 0.9810, Val Loss: 0.0921, Val Accuracy: 0.9659
Epoch 988/1000, Train Loss: 0.0578, Train Accuracy: 0.9811, Val Loss: 0.0923, Val Accuracy: 0.9656
Epoch 989/1000, Train Loss: 0.0572, Train Accuracy: 0.9811, Val Loss: 0.0923, Val Accuracy: 0.9659
Epoch 990/1000, Train Loss: 0.0574, Train Accuracy: 0.9816, Val Loss: 0.0919, Val Accuracy: 0.9657
Epoch 991/1000, Train Loss: 0.0570, Train Accuracy: 0.9818, Val Loss: 0.0923, Val Accuracy: 0.9654
Epoch 992/1000, Train Loss: 0.0577, Train Accuracy: 0.9817, Val Loss: 0.0922, Val Accuracy: 0.9654
Epoch 993/1000, Train Loss: 0.0574, Train Accuracy: 0.9815, Val Loss: 0.0922, Val Accuracy: 0.9659
Epoch 994/1000, Train Loss: 0.0590, Train Accuracy: 0.9804, Val Loss: 0.0923, Val Accuracy: 0.9659
Epoch 995/1000, Train Loss: 0.0568, Train Accuracy: 0.9810, Val Loss: 0.0923, Val Accuracy: 0.9659
Epoch 996/1000, Train Loss: 0.0579, Train Accuracy: 0.9811, Val Loss: 0.0919, Val Accuracy: 0.9664
Epoch 997/1000, Train Loss: 0.0565, Train Accuracy: 0.9818, Val Loss: 0.0922, Val Accuracy: 0.9663
Epoch 998/1000, Train Loss: 0.0563, Train Accuracy: 0.9821, Val Loss: 0.0923, Val Accuracy: 0.9663
Epoch 999/1000, Train Loss: 0.0575, Train Accuracy: 0.9815, Val Loss: 0.0920, Val Accuracy: 0.9666
Epoch 1000/1000, Train Loss: 0.0565, Train Accuracy: 0.9820, Val Loss: 0.0919, Val Accuracy: 0.9666
In [ ]:
# Create the neural network classifier
clf = MLPClassifier(random_state=42)

# Assuming you have performed a grid search and obtained the best estimator
# Define the parameter grid
param_grid = {
    'hidden_layer_sizes': [(30,30), (10,10,10)],
    'solver': ['adam', 'sgd'],
    'alpha': [0.001, 0.01],
}

# Create the GridSearchCV object
grid_search = GridSearchCV(clf, param_grid, scoring='accuracy', cv=2)

# Fit the GridSearchCV object to the data
grid_search.fit(X_train_merged_nn, y_train_merged_nn)


# Save the best estimator in best_nn
best_nn_DS_merged = grid_search.best_estimator_
print(best_nn_DS_merged)

c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(

MLPClassifier(alpha=0.001, hidden_layer_sizes=(30, 30), random_state=42,
              solver='sgd')

c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
In [ ]:
torch.save(model_merged_DS.state_dict(), best_model_path_merged)
¶

Test for the model dropseq dataset merged

This below is the step for testing our neural network with the merged dataset.

As before the model is set to evaluation mode, and test data is converted to PyTorch tensors.

A forward pass is performed on the test data to obtain predictions. The test loss is calculated using the same loss function as before and predictions are converted to binary values by applying a sigmoid function followed by rounding. Finally, The number of correct predictions is counted and used to compute the test accuracy.

In [ ]:
# Load the best model
model_merged_DS.load_state_dict(torch.load('best_model_final_merged.pth'))

# Evaluate on test data
model_merged_DS.eval()
X_test_tensor_merged = torch.tensor(X_test_merged, dtype=torch.float32)
y_test_tensor_merged = torch.tensor(y_test_merged, dtype=torch.float32).unsqueeze(1)


with torch.no_grad():
    test_output = model_merged_DS(X_test_tensor_merged)
    test_loss = criterion(test_output, y_test_tensor_merged)
    test_predictions = torch.round(torch.sigmoid(test_output))
    test_correct = (test_predictions == y_test_tensor_merged).sum().item()
    test_total = y_test_tensor_merged.size(0)
    test_acc = test_correct / test_total

print(f'Test Loss: {test_loss.item():.4f}, Test Accuracy: {test_acc:.4f}')

Test Loss: 0.0930, Test Accuracy: 0.9652

¶

4.6) PREDICTORS ON ALL DATASET MERGED

We then decided to try to build a unique big classifier that is able to predict the target of all the four different datasets.

In order to do it, we needed to ensure that all the normalizations were the same, so we applied the same method to all the files to be consistent and to create a big dataset that is consistent with the noralization for all of its components.

To make the values remain non negativ, as having negative entries would not make any sense for our dataset sinc any entry represents how many times the correspondent gene is found in the RNA/DNA extracted from the cell, we used the MinMaxScaler.

We used it as it preserves the shape of the original distribution of the data and it ensures that all features are on the same scale, preventing features with larger ranges from dominating those with smaller ranges during model training.

We have then substituted the NaN values (generated from the fact that there were more than 8000 different genes) with 0 values, as in our case makes sense, since it represents the lack of that specific gene.

The division in training and test data than remains the same.

In [ ]:
#i want to join the 4 datasets into a bigger one to try to build a general model
#in order to be consistent i need to perform a rescaling of the data for all the 4 datasets
#i will use the MinMaxScaler for that

scaler = MinMaxScaler()
df_SS_MCF7_norm_filtT.iloc[ : , :-1] = scaler.fit_transform(df_SS_MCF7_norm_filtT.iloc[ : , :-1])
df_SS_HCC1806_norm_filtT.iloc[ : , :-1] = scaler.fit_transform(df_SS_HCC1806_norm_filtT.iloc[ : , :-1])
df_DS_HCC1806_norm_filtT.iloc[ : , :-1] = scaler.fit_transform(df_DS_HCC1806_norm_filtT.iloc[ : , :-1])
df_DS_MCF7_norm_filtT.iloc[ : , :-1] = scaler.fit_transform(df_DS_MCF7_norm_filtT.iloc[ : , :-1])

#now i can join the dataframes

df_all = pd.concat([df_SS_MCF7_norm_filtT, df_SS_HCC1806_norm_filtT, df_DS_HCC1806_norm_filtT, df_DS_MCF7_norm_filtT])

#i replace the NaN values with 0
df_all.fillna(0, inplace=True)

df_all['target'] = np.where(df_all.index.str.contains('Hypo'), 1, 0)
df_all

#now i split the data into training and testing data, with a balance of 70% training and 30% testing
X_all = df_all.drop('target', axis=1)
y_all = df_all['target']

X_train_all, X_test_all, y_train_all, y_test_all = train_test_split(X_all, y_all, test_size=0.3, random_state=42)

¶

4.6.1) SVM

We used same approach used for SmartSeq MCF7, see 4.1.1.

In [ ]:
# build SVM on ENTIRE DATASET  min

# Define the SVM classifier
svm = SVC(random_state=42)

# Define the parameter grid for GridSearchCV
param_grid = {'kernel': ['linear', 'rbf']}
grid_search_svm = GridSearchCV(svm, param_grid, cv=2)

#outer_cv = KFold(n_splits=2, shuffle=True, random_state=42)

# Perform the cross validation
scores_svm = cross_val_score(grid_search_svm, X_train_all, y_train_all, cv=2)

# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores_svm))

Mean accuracy of nested cross validation:  0.9440080877206626
In [ ]:
grid_search_svm.fit(X_train_all, y_train_all)
Out[ ]:
GridSearchCV(cv=2, estimator=SVC(random_state=42),
             param_grid={'kernel': ['linear', 'rbf']})
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GridSearchCV(cv=2, estimator=SVC(random_state=42),
             param_grid={'kernel': ['linear', 'rbf']})
SVC(random_state=42)
SVC(random_state=42)
In [ ]:
# Perform GridSearchCV on the entire dataset to get the best parameters and fit the model
print("Best kernel: ", grid_search_svm.best_params_)
print("Best accuracy: ", grid_search_svm.best_score_)
best_svm_all = grid_search_svm.best_estimator_
print("Best model: ", best_svm_all)

# Test the model on the test dataset
print("Test accuracy: ", best_svm_all.score(X_test_all, y_test_all))

Best kernel:  {'kernel': 'rbf'}
Best accuracy:  0.9440080877206626
Best model:  SVC(random_state=42)
Test accuracy:  0.9543639992741789

¶

4.6.2) Random Forest

Here we find the building of the random forest classifier, following exactly the same passages of all the other ones.

The performance is quite good, it achieves a 94,81%, that we considered good as the joint dataset had huge dimensions.

Also in this most important feature ranking we can notice the presence of the genes BCYRN1, MT-RNR2 amd MALAT1.

In [ ]:
#i build the random forest classifier for the general dataset
# Define the random forest classifier
rf = RandomForestClassifier(random_state=42)

# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [30, 50, 70], 'max_depth': [10, 20, 30]}
grid_search = GridSearchCV(rf, param_grid, cv=5)

outer_cv = KFold(n_splits=2, shuffle=True, random_state=42)

# Perform the cross validation
scores = cross_val_score(grid_search, X_train_all, y_train_all, cv=outer_cv)

# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))    

# Perform GridSearchCV on the entire dataset to get the best parameters and fit the model
grid_search.fit(X_train_all, y_train_all)
print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
print("Best model: ", grid_search.best_estimator_)

#i want to test the model on the test dataset
print("Test accuracy: ", grid_search.best_estimator_.score(X_test_all, y_test_all))

# Print the feature importances
importances = grid_search.best_estimator_.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_all.shape[1]):
    print("%d. feature %s (%f)" % (f + 1, X_train_all.columns[indices[f]], importances[indices[f]]))

best_rf_all = grid_search.best_estimator_

Mean accuracy of nested cross validation:  0.9400419939342095
Best hyperparameters:  {'max_depth': 30, 'n_estimators': 70}
Best accuracy:  0.9434637032503375
Best model:  RandomForestClassifier(max_depth=30, n_estimators=70, random_state=42)
Test accuracy:  0.9481037924151696
Feature ranking:
1. feature "KRT19" (0.021781)
2. feature "TMSB10" (0.018618)
3. feature "TFF1" (0.017516)
4. feature "GAPDH" (0.015135)
5. feature "BCYRN1" (0.015076)
6. feature "MT-RNR2" (0.014077)
7. feature "PGK1" (0.013782)
8. feature "NDRG1" (0.013346)
9. feature "LGALS1" (0.013215)
10. feature "ENO1" (0.012655)
11. feature "FTL" (0.011469)
12. feature "TPI1" (0.009811)
13. feature "S100A10" (0.009186)
14. feature "RPS19" (0.009088)
15. feature "MALAT1" (0.008743)
16. feature "IGFBP3" (0.008361)
17. feature "MT-RNR1" (0.007893)
18. feature "RPS5" (0.007762)
19. feature "LDHA" (0.007553)
20. feature "ACTB" (0.007388)
21. feature "RPL35" (0.007065)
22. feature "RPS14" (0.006883)
23. feature "RPL13" (0.006779)
24. feature "RPL39" (0.006470)
25. feature "RPL41" (0.006367)
26. feature "S100A11" (0.006233)
27. feature "SERF2" (0.006110)
28. feature "TOB1" (0.006064)
29. feature "RPS28" (0.005967)
30. feature "TMEM258" (0.005753)
31. feature "SET" (0.005742)
32. feature "RPLP1" (0.005571)
33. feature "PKM" (0.005370)
34. feature "NCL" (0.005036)
35. feature "BNIP3" (0.005026)
36. feature "AKR1C1" (0.004855)
37. feature "HSPD1" (0.004844)
38. feature "HSP90B1" (0.004661)
39. feature "MT2A" (0.004595)
40. feature "P4HA1" (0.004579)
41. feature "AKR1C2" (0.004575)
42. feature "RPLP2" (0.004554)
43. feature "H4C3" (0.004549)
44. feature "HSP90AA1" (0.004503)
45. feature "ROMO1" (0.004473)
46. feature "HES1" (0.004470)
47. feature "RPS27" (0.004412)
48. feature "RPL35A" (0.004331)
49. feature "RPL34" (0.004227)
50. feature "RPL12" (0.004184)
51. feature "WDR43" (0.004164)
52. feature "FGF23" (0.003958)
53. feature "RPL15" (0.003924)
54. feature "DSP" (0.003894)
55. feature "MT-CYB" (0.003830)
56. feature "HSP90AB1" (0.003826)
57. feature "MT-CO3" (0.003818)
58. feature "RPL36" (0.003816)
59. feature "RPS15" (0.003744)
60. feature "RPS15A" (0.003528)
61. feature "GPM6A" (0.003433)
62. feature "TMEM64" (0.003413)
63. feature "B4GALT1" (0.003406)
64. feature "TFF3" (0.003353)
65. feature "RPS16" (0.003268)
66. feature "UBA52" (0.003217)
67. feature "MT-ND3" (0.003204)
68. feature "SNRPD2" (0.003188)
69. feature "RPL8" (0.003158)
70. feature "RPL30" (0.003118)
71. feature "RPS12" (0.003115)
72. feature "CALM2" (0.002932)
73. feature "HSPB1" (0.002920)
74. feature "MT-ATP6" (0.002912)
75. feature "S100A6" (0.002869)
76. feature "MT-ND4" (0.002850)
77. feature "MT-ND4L" (0.002806)
78. feature "BCAS3" (0.002725)
79. feature "KRT18" (0.002721)
80. feature "PKIB" (0.002708)
81. feature "PARD6B" (0.002659)
82. feature "MT-CO2" (0.002624)
83. feature "EIF3J" (0.002553)
84. feature "HNRNPAB" (0.002536)
85. feature "YWHAZ" (0.002515)
86. feature "ELOB" (0.002476)
87. feature "CSDE1" (0.002442)
88. feature "RPL23" (0.002376)
89. feature "RPL37A" (0.002368)
90. feature "KRT8" (0.002308)
91. feature "DNAJA1" (0.002293)
92. feature "RPS2" (0.002206)
93. feature "TPD52L1" (0.002140)
94. feature "NPM1P40" (0.002121)
95. feature "PSMA7" (0.002096)
96. feature "RPL37" (0.002096)
97. feature "BNIP3L" (0.002091)
98. feature "UQCRQ" (0.002056)
99. feature "AHNAK2" (0.002035)
100. feature "CAV1" (0.002025)
101. feature "COX7A2" (0.002007)
102. feature "MARCKS" (0.001989)
103. feature "PRRC2C" (0.001973)
104. feature "TRIM37" (0.001959)
105. feature "CAST" (0.001906)
106. feature "MT-ND5" (0.001895)
107. feature "TPT1" (0.001887)
108. feature "SOX4" (0.001853)
109. feature "PTMS" (0.001852)
110. feature "YBX1" (0.001852)
111. feature "YWHAB" (0.001850)
112. feature "RPL27A" (0.001840)
113. feature "EIF4G2" (0.001837)
114. feature "HNRNPU" (0.001794)
115. feature "ZC3H15" (0.001769)
116. feature "HNRNPA2B1" (0.001735)
117. feature "GPI" (0.001724)
118. feature "NCOA3" (0.001710)
119. feature "CBX3" (0.001709)
120. feature "RPL10" (0.001708)
121. feature "HSPH1" (0.001695)
122. feature "TPM3" (0.001642)
123. feature "NDUFB2" (0.001600)
124. feature "H1-3" (0.001539)
125. feature "RPL28" (0.001531)
126. feature "PFDN4" (0.001521)
127. feature "RPS8" (0.001489)
128. feature "TMSB4X" (0.001465)
129. feature "RPS3" (0.001457)
130. feature "ATP5MK" (0.001435)
131. feature "CACNA1A" (0.001418)
132. feature "PTP4A2" (0.001414)
133. feature "EZR" (0.001410)
134. feature "EGLN3" (0.001409)
135. feature "GOLGA4" (0.001397)
136. feature "RPL11" (0.001386)
137. feature "ATP5ME" (0.001381)
138. feature "HDGF" (0.001378)
139. feature "H1-5" (0.001372)
140. feature "PTPN11" (0.001370)
141. feature "RPS6KB1" (0.001369)
142. feature "MT1X" (0.001365)
143. feature "RPL21" (0.001338)
144. feature "PRRG3" (0.001307)
145. feature "CALR" (0.001300)
146. feature "DEK" (0.001291)
147. feature "XBP1" (0.001260)
148. feature "LARP1" (0.001227)
149. feature "NCALD" (0.001196)
150. feature "ANP32B" (0.001191)
151. feature "GSTP1" (0.001182)
152. feature "BPTF" (0.001181)
153. feature "MKI67" (0.001178)
154. feature "ATP1A1" (0.001169)
155. feature "H1-4" (0.001169)
156. feature "HIF1A" (0.001158)
157. feature "RPL18" (0.001153)
158. feature "NEAT1" (0.001145)
159. feature "MT-ND6" (0.001140)
160. feature "ZNF302" (0.001130)
161. feature "RPS21" (0.001128)
162. feature "C4orf48" (0.001126)
163. feature "TPM4" (0.001108)
164. feature "H2AC12" (0.001107)
165. feature "PDIA3" (0.001103)
166. feature "MT-TQ" (0.001099)
167. feature "CENPF" (0.001092)
168. feature "MT-ND2" (0.001084)
169. feature "BTBD9" (0.001073)
170. feature "POLR2L" (0.001069)
171. feature "PABPC1" (0.001053)
172. feature "CAPZA1" (0.001051)
173. feature "DSCAM-AS1" (0.001049)
174. feature "HNRNPM" (0.001046)
175. feature "FOXP1" (0.001042)
176. feature "RAC1" (0.001027)
177. feature "HSPA5" (0.001026)
178. feature "EEF2" (0.001023)
179. feature "COX7C" (0.001013)
180. feature "S100A2" (0.001007)
181. feature "RPSA" (0.001003)
182. feature "CKS2" (0.000995)
183. feature "H1-2" (0.000991)
184. feature "ATAD2" (0.000975)
185. feature "DNMT1" (0.000962)
186. feature "PDAP1" (0.000954)
187. feature "NSD1" (0.000948)
188. feature "ATP5F1E" (0.000939)
189. feature "RPL18A" (0.000929)
190. feature "ATP5MG" (0.000920)
191. feature "LARP4B" (0.000918)
192. feature "GNL3L" (0.000910)
193. feature "MT-CO1" (0.000909)
194. feature "RPL29" (0.000877)
195. feature "TOP2A" (0.000873)
196. feature "STMN1" (0.000866)
197. feature "RPS29" (0.000866)
198. feature "NFE2L2" (0.000865)
199. feature "TPX2" (0.000856)
200. feature "EIF5B" (0.000851)
201. feature "GATA3" (0.000824)
202. feature "KIF5B" (0.000821)
203. feature "PPDPF" (0.000810)
204. feature "PLEC" (0.000810)
205. feature "KRT7" (0.000806)
206. feature "COPS9" (0.000800)
207. feature "APLP2" (0.000800)
208. feature "HMGB2" (0.000791)
209. feature "H1-1" (0.000788)
210. feature "H2AC20" (0.000787)
211. feature "EEF1A1" (0.000767)
212. feature "TRIM44" (0.000764)
213. feature "RTF1" (0.000762)
214. feature "C9orf78" (0.000761)
215. feature "NMD3" (0.000752)
216. feature "PAK2" (0.000729)
217. feature "DAAM1" (0.000725)
218. feature "MYH9" (0.000707)
219. feature "PPM1G" (0.000695)
220. feature "NEDD8" (0.000685)
221. feature "POLR2J" (0.000684)
222. feature "RHOBTB3" (0.000678)
223. feature "GTF3A" (0.000677)
224. feature "PPP1R14B" (0.000672)
225. feature "BAP1" (0.000669)
226. feature "ZBTB20" (0.000664)
227. feature "C7orf50" (0.000662)
228. feature "NRBP1" (0.000656)
229. feature "KRT10" (0.000655)
230. feature "ERO1A" (0.000652)
231. feature "ARF3" (0.000650)
232. feature "VMP1" (0.000650)
233. feature "ARPP19" (0.000647)
234. feature "AREG" (0.000642)
235. feature "BAZ1B" (0.000642)
236. feature "MT-TV" (0.000634)
237. feature "UQCR11" (0.000632)
238. feature "UQCC2" (0.000622)
239. feature "MT-ND1" (0.000621)
240. feature "PPIG" (0.000620)
241. feature "PFN1" (0.000618)
242. feature "KCNJ3" (0.000618)
243. feature "PLOD2" (0.000617)
244. feature "UBC" (0.000612)
245. feature "NEDD4L" (0.000610)
246. feature "TPM1" (0.000608)
247. feature "AHNAK" (0.000608)
248. feature "FABP5" (0.000607)
249. feature "ACTG1" (0.000604)
250. feature "ZNF217" (0.000600)
251. feature "H2AC11" (0.000596)
252. feature "BRD4" (0.000596)
253. feature "C19orf53" (0.000595)
254. feature "HMGA1" (0.000595)
255. feature "YBX3" (0.000590)
256. feature "MT-ATP8" (0.000589)
257. feature "STRBP" (0.000587)
258. feature "CNNM2" (0.000586)
259. feature "HEPACAM" (0.000584)
260. feature "SLC9A3R1" (0.000582)
261. feature "CSTB" (0.000581)
262. feature "NRIP1" (0.000581)
263. feature "LIMCH1" (0.000581)
264. feature "DDIT4" (0.000571)
265. feature "SMC4" (0.000565)
266. feature "PDIA6" (0.000564)
267. feature "HSBP1" (0.000560)
268. feature "C4orf3" (0.000559)
269. feature "MGRN1" (0.000554)
270. feature "EIF3A" (0.000549)
271. feature "NAA50" (0.000548)
272. feature "NCOR2" (0.000543)
273. feature "KCNJ2" (0.000541)
274. feature "H3-3A" (0.000538)
275. feature "SLC25A24" (0.000537)
276. feature "GNAS" (0.000537)
277. feature "FOSL1" (0.000530)
278. feature "RAD23B" (0.000528)
279. feature "PAPOLA" (0.000524)
280. feature "CDV3" (0.000518)
281. feature "CLIP1" (0.000514)
282. feature "G6PD" (0.000512)
283. feature "TRIP12" (0.000511)
284. feature "OAZ1" (0.000508)
285. feature "MRPL33" (0.000506)
286. feature "IGFBP5" (0.000506)
287. feature "LEO1" (0.000505)
288. feature "KDM1A" (0.000501)
289. feature "ANXA1" (0.000497)
290. feature "AURKA" (0.000497)
291. feature "CEP55" (0.000497)
292. feature "GDPGP1" (0.000493)
293. feature "SPATS2L" (0.000488)
294. feature "KRT14" (0.000485)
295. feature "HSPA4" (0.000484)
296. feature "LAMB3" (0.000483)
297. feature "SVIP" (0.000483)
298. feature "KRT13" (0.000480)
299. feature "TAOK3" (0.000476)
300. feature "METAP2" (0.000476)
301. feature "MZT2B" (0.000473)
302. feature "POLR2A" (0.000472)
303. feature "SFN" (0.000471)
304. feature "S100P" (0.000471)
305. feature "MAP3K13" (0.000468)
306. feature "RNPS1" (0.000467)
307. feature "NBN" (0.000466)
308. feature "PRMT2" (0.000461)
309. feature "LRRC59" (0.000460)
310. feature "H19" (0.000459)
311. feature "CAVIN1" (0.000458)
312. feature "HCFC1" (0.000453)
313. feature "PTBP3" (0.000452)
314. feature "SNX27" (0.000443)
315. feature "GSPT1" (0.000442)
316. feature "EIF2AK1" (0.000439)
317. feature "PLBD2" (0.000438)
318. feature "ENAH" (0.000435)
319. feature "SLC2A1" (0.000431)
320. feature "CDK2AP1" (0.000426)
321. feature "FAM162A" (0.000424)
322. feature "TSR1" (0.000424)
323. feature "CDKN3" (0.000421)
324. feature "H1-0" (0.000418)
325. feature "BASP1" (0.000412)
326. feature "C8orf33" (0.000412)
327. feature "GPATCH4" (0.000410)
328. feature "GBP1P1" (0.000408)
329. feature "MARCHF6" (0.000406)
330. feature "CSTA" (0.000404)
331. feature "ATMIN" (0.000403)
332. feature "LINC01304" (0.000400)
333. feature "CAMSAP2" (0.000396)
334. feature "CD47" (0.000395)
335. feature "TBCA" (0.000394)
336. feature "NDUFB10" (0.000394)
337. feature "MED13" (0.000388)
338. feature "KRT5" (0.000386)
339. feature "CAVIN3" (0.000385)
340. feature "RALBP1" (0.000384)
341. feature "APOOL" (0.000382)
342. feature "RPS27L" (0.000382)
343. feature "PRPF40A" (0.000380)
344. feature "MAN1A2" (0.000379)
345. feature "TCEAL4" (0.000378)
346. feature "RPL27" (0.000377)
347. feature "TMA7" (0.000377)
348. feature "AP2S1" (0.000377)
349. feature "METRN" (0.000375)
350. feature "TAF13" (0.000374)
351. feature "RAB13" (0.000373)
352. feature "RAD21" (0.000372)
353. feature "SLK" (0.000370)
354. feature "HNRNPUL1" (0.000370)
355. feature "TAF15" (0.000368)
356. feature "WWP1" (0.000364)
357. feature "AP1G1" (0.000364)
358. feature "LBH" (0.000363)
359. feature "CCNI" (0.000362)
360. feature "PMEPA1" (0.000361)
361. feature "RPS6KA6" (0.000360)
362. feature "PSMD14" (0.000359)
363. feature "FAM120A" (0.000358)
364. feature "TARS1" (0.000355)
365. feature "ZHX1" (0.000354)
366. feature "MYL12B" (0.000353)
367. feature "URI1" (0.000353)
368. feature "DKC1" (0.000353)
369. feature "ROCK2" (0.000352)
370. feature "LXN" (0.000352)
371. feature "CENPU" (0.000349)
372. feature "ZFP36L1" (0.000348)
373. feature "SYNE2" (0.000348)
374. feature "GUK1" (0.000347)
375. feature "MYO5C" (0.000347)
376. feature "ANP32E" (0.000345)
377. feature "BTN3A2" (0.000344)
378. feature "PROSER1" (0.000343)
379. feature "H4C5" (0.000342)
380. feature "RBM25" (0.000342)
381. feature "NDUFS6" (0.000340)
382. feature "PRRC2A" (0.000339)
383. feature "DDX21" (0.000337)
384. feature "TIMM8B" (0.000337)
385. feature "MLLT3" (0.000336)
386. feature "POLR2I" (0.000331)
387. feature "FAM83A" (0.000331)
388. feature "S100A16" (0.000330)
389. feature "JAK1" (0.000330)
390. feature "HNRNPD" (0.000329)
391. feature "PAIP2" (0.000328)
392. feature "MRPS34" (0.000327)
393. feature "ATOX1" (0.000327)
394. feature "DDX54" (0.000326)
395. feature "PHF3" (0.000326)
396. feature "SYTL2" (0.000325)
397. feature "SETD2" (0.000323)
398. feature "BOD1L1" (0.000321)
399. feature "MAP7" (0.000320)
400. feature "PUS7" (0.000317)
401. feature "METTL26" (0.000317)
402. feature "USP8" (0.000315)
403. feature "ANKRD11" (0.000313)
404. feature "REST" (0.000313)
405. feature "JUP" (0.000311)
406. feature "IRAK1" (0.000311)
407. feature "HILPDA" (0.000311)
408. feature "RB1CC1" (0.000310)
409. feature "DYNLL2" (0.000309)
410. feature "ZNRF1" (0.000309)
411. feature "MTND1P23" (0.000308)
412. feature "CCDC34" (0.000308)
413. feature "NUP62" (0.000307)
414. feature "ZNF121" (0.000307)
415. feature "CDK2AP2" (0.000304)
416. feature "CTBP2" (0.000303)
417. feature "RPS26" (0.000303)
418. feature "ANKRD52" (0.000303)
419. feature "FAM177A1" (0.000300)
420. feature "CAMK2N1" (0.000300)
421. feature "PDLIM1" (0.000299)
422. feature "RAP2B" (0.000299)
423. feature "VTI1B" (0.000298)
424. feature "GADD45GIP1" (0.000297)
425. feature "MT1E" (0.000297)
426. feature "C6orf62" (0.000296)
427. feature "RABEP1" (0.000296)
428. feature "FOSL2" (0.000294)
429. feature "CAP1" (0.000294)
430. feature "H2BC4" (0.000294)
431. feature "EPPK1" (0.000293)
432. feature "UBE2Q1" (0.000291)
433. feature "CLTB" (0.000291)
434. feature "KPNA1" (0.000291)
435. feature "TCEAL9" (0.000290)
436. feature "UBE2K" (0.000290)
437. feature "HTATSF1" (0.000289)
438. feature "KPNA2" (0.000289)
439. feature "SLTM" (0.000289)
440. feature "H2AZ2" (0.000287)
441. feature "EEA1" (0.000287)
442. feature "SENP7" (0.000287)
443. feature "CENPB" (0.000285)
444. feature "ZMYND11" (0.000285)
445. feature "UGDH" (0.000284)
446. feature "PHACTR2" (0.000284)
447. feature "SOS1" (0.000281)
448. feature "SEPTIN9" (0.000281)
449. feature "RBM23" (0.000281)
450. feature "RPL7A" (0.000281)
451. feature "GOLGA3" (0.000280)
452. feature "KMT2D" (0.000279)
453. feature "ITPRID2" (0.000279)
454. feature "USP32" (0.000278)
455. feature "NUDT1" (0.000278)
456. feature "RGPD4-AS1" (0.000277)
457. feature "ATRX" (0.000275)
458. feature "FLNA" (0.000274)
459. feature "CSK" (0.000274)
460. feature "SYT14" (0.000274)
461. feature "ANP32A" (0.000273)
462. feature "TAGLN2" (0.000273)
463. feature "ROCK1" (0.000271)
464. feature "GPX4" (0.000271)
465. feature "NIPBL" (0.000271)
466. feature "MDM2" (0.000270)
467. feature "LUZP1" (0.000269)
468. feature "DCTN1" (0.000269)
469. feature "LRRFIP2" (0.000269)
470. feature "RHOD" (0.000269)
471. feature "DTYMK" (0.000267)
472. feature "CMSS1" (0.000266)
473. feature "LMNB2" (0.000265)
474. feature "PAQR8" (0.000265)
475. feature "SUDS3" (0.000265)
476. feature "HNRNPH3" (0.000265)
477. feature "MZT2A" (0.000264)
478. feature "STC2" (0.000264)
479. feature "SLC6A8" (0.000264)
480. feature "OPTN" (0.000263)
481. feature "SLC25A48" (0.000262)
482. feature "VIT" (0.000262)
483. feature "FAM102A" (0.000262)
484. feature "ILF3" (0.000262)
485. feature "CARMIL1" (0.000262)
486. feature "ZBTB7A" (0.000261)
487. feature "H3C2" (0.000261)
488. feature "NORAD" (0.000261)
489. feature "PGAM1" (0.000259)
490. feature "ZC3H13" (0.000259)
491. feature "ZCRB1" (0.000258)
492. feature "GATAD2A" (0.000258)
493. feature "NONO" (0.000256)
494. feature "RBBP6" (0.000256)
495. feature "PFKP" (0.000256)
496. feature "DNAJC2" (0.000256)
497. feature "OIP5-AS1" (0.000255)
498. feature "NCOR1" (0.000254)
499. feature "FGFR1OP2" (0.000254)
500. feature "SSRP1" (0.000254)
501. feature "TNIP2" (0.000254)
502. feature "SUMO3" (0.000253)
503. feature "CNBP" (0.000253)
504. feature "MT-TS1" (0.000253)
505. feature "ZNF688" (0.000253)
506. feature "R3HDM2" (0.000253)
507. feature "SART1" (0.000252)
508. feature "SIVA1" (0.000252)
509. feature "SRGAP1" (0.000252)
510. feature "FEM1A" (0.000250)
511. feature "SRM" (0.000250)
512. feature "SEM1" (0.000249)
513. feature "FUBP3" (0.000248)
514. feature "PSMC5" (0.000247)
515. feature "SMIM10L1" (0.000247)
516. feature "SLC25A37" (0.000245)
517. feature "ZC3H18" (0.000245)
518. feature "ATN1" (0.000244)
519. feature "MRPL55" (0.000242)
520. feature "YTHDC1" (0.000241)
521. feature "DBF4" (0.000241)
522. feature "AP1M1" (0.000240)
523. feature "KIF1C" (0.000239)
524. feature "KPNA4" (0.000237)
525. feature "PHLDA2" (0.000235)
526. feature "AKT1S1" (0.000232)
527. feature "RSRC2" (0.000231)
528. feature "NACC1" (0.000231)
529. feature "MTCH1" (0.000231)
530. feature "CMIP" (0.000231)
531. feature "CDC37" (0.000231)
532. feature "PKP4" (0.000231)
533. feature "TSPYL1" (0.000231)
534. feature "PAQR7" (0.000230)
535. feature "MCM4" (0.000230)
536. feature "ZNF480" (0.000229)
537. feature "SNHG9" (0.000229)
538. feature "ANKIB1" (0.000229)
539. feature "WNK1" (0.000229)
540. feature "MAP4K4" (0.000229)
541. feature "PRKACA" (0.000228)
542. feature "COTL1" (0.000228)
543. feature "CTNND1" (0.000228)
544. feature "ETF1" (0.000227)
545. feature "KAT7" (0.000227)
546. feature "RBX1" (0.000226)
547. feature "PSAP" (0.000224)
548. feature "COX8A" (0.000223)
549. feature "TNRC6A" (0.000223)
550. feature "MAFF" (0.000223)
551. feature "FUT11" (0.000223)
552. feature "SHOX" (0.000223)
553. feature "BRIP1" (0.000222)
554. feature "MYBL2" (0.000222)
555. feature "STARD7" (0.000222)
556. feature "SNRNP70" (0.000221)
557. feature "MXI1" (0.000221)
558. feature "KMT5B" (0.000220)
559. feature "SPIN1" (0.000220)
560. feature "JUN" (0.000219)
561. feature "IFI27L2" (0.000219)
562. feature "THRB" (0.000219)
563. feature "KIF3B" (0.000219)
564. feature "RANGAP1" (0.000218)
565. feature "FAM107B" (0.000218)
566. feature "NDUFAF8" (0.000214)
567. feature "KIF21A" (0.000214)
568. feature "CNN3" (0.000212)
569. feature "NFIC" (0.000212)
570. feature "FGD5-AS1" (0.000212)
571. feature "UPF3B" (0.000211)
572. feature "RRS1" (0.000211)
573. feature "KRT17" (0.000211)
574. feature "C5orf63" (0.000211)
575. feature "TMEM160" (0.000211)
576. feature "PDCD4" (0.000211)
577. feature "NIBAN2" (0.000210)
578. feature "HMGN3" (0.000210)
579. feature "EGR1" (0.000210)
580. feature "BDP1" (0.000210)
581. feature "SUPT5H" (0.000210)
582. feature "DNAJC21" (0.000209)
583. feature "PPP4R3A" (0.000207)
584. feature "SPAG9" (0.000206)
585. feature "ARID5B" (0.000206)
586. feature "SMARCC1" (0.000206)
587. feature "CDC42BPA" (0.000206)
588. feature "SMARCB1" (0.000206)
589. feature "ARID4B" (0.000205)
590. feature "AMOTL1" (0.000204)
591. feature "TSPO" (0.000204)
592. feature "INPP4B" (0.000204)
593. feature "KIAA1522" (0.000203)
594. feature "UBE2Q2" (0.000203)
595. feature "PALLD" (0.000202)
596. feature "GSE1" (0.000202)
597. feature "ZNF316" (0.000202)
598. feature "SMC6" (0.000201)
599. feature "ISCU" (0.000201)
600. feature "ZMIZ1" (0.000201)
601. feature "USP1" (0.000200)
602. feature "FASN" (0.000200)
603. feature "WBP4" (0.000199)
604. feature "GNB1" (0.000199)
605. feature "CCDC167" (0.000199)
606. feature "PPP4R2" (0.000199)
607. feature "STARD10" (0.000198)
608. feature "GOLGA5" (0.000197)
609. feature "MTA2" (0.000196)
610. feature "SAMD4A" (0.000196)
611. feature "BCCIP" (0.000196)
612. feature "SCRIB" (0.000196)
613. feature "WSB2" (0.000195)
614. feature "MPHOSPH6" (0.000195)
615. feature "LYAR" (0.000195)
616. feature "TNIP1" (0.000195)
617. feature "PREX1" (0.000195)
618. feature "PFKFB3" (0.000194)
619. feature "YTHDF1" (0.000194)
620. feature "AP3D1" (0.000194)
621. feature "NMT1" (0.000192)
622. feature "PPP2R5E" (0.000192)
623. feature "ZNF703" (0.000192)
624. feature "RRAS" (0.000192)
625. feature "MIR4458HG" (0.000192)
626. feature "UQCC3" (0.000192)
627. feature "NINJ1" (0.000192)
628. feature "TMEM238" (0.000191)
629. feature "IGFL2-AS1" (0.000190)
630. feature "EIF4E" (0.000190)
631. feature "R3HDM1" (0.000190)
632. feature "TUBD1" (0.000190)
633. feature "NIN" (0.000189)
634. feature "EPB41L1" (0.000189)
635. feature "TASOR2" (0.000189)
636. feature "RBFOX2" (0.000189)
637. feature "PCP4" (0.000188)
638. feature "HK1" (0.000188)
639. feature "FBP1" (0.000188)
640. feature "RFC1" (0.000188)
641. feature "RBBP8" (0.000187)
642. feature "ANKRD17" (0.000187)
643. feature "CFAP36" (0.000186)
644. feature "TOP2B" (0.000186)
645. feature "CRNDE" (0.000185)
646. feature "CHMP4B" (0.000185)
647. feature "DDX23" (0.000185)
648. feature "CFAP251" (0.000184)
649. feature "KRT80" (0.000183)
650. feature "CDC42SE1" (0.000183)
651. feature "PYGO2" (0.000183)
652. feature "NR2F6" (0.000183)
653. feature "HCFC1R1" (0.000182)
654. feature "SENP6" (0.000182)
655. feature "FAM32A" (0.000181)
656. feature "ATP2A2" (0.000180)
657. feature "TLK2" (0.000180)
658. feature "MT-TY" (0.000180)
659. feature "NET1" (0.000179)
660. feature "ARHGDIA" (0.000178)
661. feature "MIR663AHG" (0.000178)
662. feature "NECAB1" (0.000178)
663. feature "HSPBP1" (0.000177)
664. feature "SMARCD2" (0.000177)
665. feature "SMG7" (0.000177)
666. feature "PHF20L1" (0.000177)
667. feature "PRR11" (0.000177)
668. feature "CLIC1" (0.000176)
669. feature "MDM4" (0.000176)
670. feature "CASP8AP2" (0.000175)
671. feature "NMT2" (0.000175)
672. feature "GNAQ" (0.000175)
673. feature "ID1" (0.000175)
674. feature "MAVS" (0.000175)
675. feature "CLSPN" (0.000174)
676. feature "EIF2AK2" (0.000174)
677. feature "TF" (0.000174)
678. feature "TIMELESS" (0.000173)
679. feature "NDUFB1" (0.000173)
680. feature "RAB35" (0.000172)
681. feature "LAGE3" (0.000172)
682. feature "ANAPC16" (0.000172)
683. feature "PRPF19" (0.000172)
684. feature "ESCO1" (0.000172)
685. feature "IRF2BPL" (0.000171)
686. feature "RRP15" (0.000171)
687. feature "ARHGAP5" (0.000170)
688. feature "MRPL14" (0.000170)
689. feature "HK2" (0.000170)
690. feature "CENPE" (0.000170)
691. feature "HNRNPA1" (0.000169)
692. feature "SAMD4B" (0.000168)
693. feature "CETN3" (0.000167)
694. feature "NEUROD1" (0.000167)
695. feature "ITGA3" (0.000167)
696. feature "KCNQ1OT1" (0.000167)
697. feature "DNMT3A" (0.000166)
698. feature "ZNF148" (0.000166)
699. feature "MYBL1" (0.000165)
700. feature "DDIT3" (0.000165)
701. feature "GARS1" (0.000165)
702. feature "GYS1" (0.000165)
703. feature "SOCS4" (0.000165)
704. feature "CPSF2" (0.000164)
705. feature "PPFIA1" (0.000164)
706. feature "CDC42BPB" (0.000164)
707. feature "PABPC4" (0.000164)
708. feature "YES1" (0.000164)
709. feature "MSANTD3" (0.000164)
710. feature "CBX6" (0.000164)
711. feature "SPDL1" (0.000163)
712. feature "ARL13B" (0.000163)
713. feature "POLE4" (0.000163)
714. feature "PDXK" (0.000163)
715. feature "USP7" (0.000162)
716. feature "LINC02367" (0.000162)
717. feature "SECISBP2" (0.000162)
718. feature "RAD54L2" (0.000162)
719. feature "S100A13" (0.000162)
720. feature "NSRP1" (0.000161)
721. feature "ITPK1" (0.000161)
722. feature "EBAG9" (0.000161)
723. feature "PACS1" (0.000161)
724. feature "EIF4EBP2" (0.000161)
725. feature "PRR34-AS1" (0.000161)
726. feature "GCC2" (0.000161)
727. feature "SKI" (0.000161)
728. feature "CCDC186" (0.000160)
729. feature "MRPS2" (0.000160)
730. feature "CHD8" (0.000159)
731. feature "SOGA1" (0.000159)
732. feature "HMGN5" (0.000159)
733. feature "UTP18" (0.000159)
734. feature "VRK3" (0.000158)
735. feature "TAOK1" (0.000158)
736. feature "H2AC16" (0.000158)
737. feature "UBP1" (0.000158)
738. feature "BAG3" (0.000156)
739. feature "RP9" (0.000156)
740. feature "H2BC9" (0.000156)
741. feature "RASGEF1B" (0.000156)
742. feature "AK4" (0.000155)
743. feature "NELFB" (0.000155)
744. feature "CAPZB" (0.000155)
745. feature "PRDM2" (0.000155)
746. feature "RAB11B" (0.000155)
747. feature "RNF168" (0.000155)
748. feature "SLPI" (0.000154)
749. feature "MARK2" (0.000153)
750. feature "RHOA" (0.000153)
751. feature "CMAS" (0.000153)
752. feature "UGCG" (0.000152)
753. feature "TIMP3" (0.000152)
754. feature "GDI1" (0.000152)
755. feature "MGP" (0.000151)
756. feature "MORN2" (0.000151)
757. feature "CAPN1" (0.000151)
758. feature "SMIM15" (0.000150)
759. feature "TWNK" (0.000149)
760. feature "SYNJ2" (0.000149)
761. feature "FBXW5" (0.000149)
762. feature "BRD7" (0.000149)
763. feature "RCC2" (0.000148)
764. feature "ZNF462" (0.000148)
765. feature "IGFBP4" (0.000148)
766. feature "EFNA2" (0.000148)
767. feature "EIF4G3" (0.000147)
768. feature "NAA10" (0.000147)
769. feature "CEP63" (0.000147)
770. feature "MT-TE" (0.000147)
771. feature "CITED2" (0.000147)
772. feature "BLCAP" (0.000146)
773. feature "PHLDB2" (0.000146)
774. feature "KDM3A" (0.000145)
775. feature "COX14" (0.000145)
776. feature "ZFC3H1" (0.000145)
777. feature "LOXL2" (0.000145)
778. feature "SNRPA" (0.000145)
779. feature "SERINC2" (0.000145)
780. feature "SIGMAR1" (0.000144)
781. feature "STX2" (0.000144)
782. feature "PLEKHB2" (0.000144)
783. feature "AHCYL1" (0.000144)
784. feature "FAM20B" (0.000143)
785. feature "MRPS33" (0.000143)
786. feature "AXL" (0.000143)
787. feature "NAA15" (0.000143)
788. feature "PHGDH" (0.000142)
789. feature "TCF20" (0.000142)
790. feature "TK1" (0.000142)
791. feature "COMMD6" (0.000142)
792. feature "SUPT6H" (0.000142)
793. feature "UTP3" (0.000142)
794. feature "MBD2" (0.000141)
795. feature "ISOC2" (0.000141)
796. feature "CTXN1" (0.000141)
797. feature "MRGBP" (0.000141)
798. feature "ITSN1" (0.000141)
799. feature "FASTK" (0.000140)
800. feature "WIPF2" (0.000140)
801. feature "SLC4A1" (0.000139)
802. feature "RNF20" (0.000139)
803. feature "NARF" (0.000139)
804. feature "RAB4A" (0.000139)
805. feature "MYO10" (0.000139)
806. feature "LINC01133" (0.000138)
807. feature "CCDC50" (0.000138)
808. feature "BRI3" (0.000138)
809. feature "ULK1" (0.000137)
810. feature "EFNA5" (0.000137)
811. feature "MT-TP" (0.000137)
812. feature "KIF14" (0.000136)
813. feature "ZCCHC9" (0.000136)
814. feature "MYH14" (0.000136)
815. feature "GTF2F1" (0.000135)
816. feature "NEUROD2" (0.000135)
817. feature "LSM4" (0.000135)
818. feature "BAZ2A" (0.000135)
819. feature "TMEM141" (0.000134)
820. feature "PRMT6" (0.000134)
821. feature "LARP7" (0.000134)
822. feature "SLC25A23" (0.000134)
823. feature "C12orf57" (0.000133)
824. feature "NDUFA2" (0.000133)
825. feature "ARHGEF26" (0.000133)
826. feature "CNOT9" (0.000133)
827. feature "LETM1" (0.000132)
828. feature "CNTRL" (0.000132)
829. feature "ESRP2" (0.000132)
830. feature "DYNC2I2" (0.000132)
831. feature "CSRP1" (0.000131)
832. feature "DNAJA2" (0.000131)
833. feature "ZNF652" (0.000131)
834. feature "RELA" (0.000131)
835. feature "MAGI3" (0.000131)
836. feature "RNF122" (0.000130)
837. feature "ZNF609" (0.000130)
838. feature "ZMYND8" (0.000130)
839. feature "MAPKAPK2" (0.000130)
840. feature "ARHGEF12" (0.000130)
841. feature "CNOT6L" (0.000130)
842. feature "TTL" (0.000129)
843. feature "SGO2" (0.000129)
844. feature "PDS5A" (0.000129)
845. feature "SPRY1" (0.000129)
846. feature "VAV2" (0.000129)
847. feature "ATP6V1E1" (0.000128)
848. feature "RAB12" (0.000128)
849. feature "SLITRK5" (0.000128)
850. feature "CEP83" (0.000128)
851. feature "CDKN1A" (0.000128)
852. feature "HRAS" (0.000128)
853. feature "BROX" (0.000128)
854. feature "FAM50A" (0.000127)
855. feature "ITGA5" (0.000127)
856. feature "RPL12P38" (0.000127)
857. feature "FAM111B" (0.000126)
858. feature "LINC01278" (0.000126)
859. feature "DPCD" (0.000126)
860. feature "SPRYD3" (0.000126)
861. feature "ODF2" (0.000126)
862. feature "EHD2" (0.000126)
863. feature "EPB41L2" (0.000126)
864. feature "GPBP1" (0.000126)
865. feature "REV3L" (0.000125)
866. feature "FAM200B" (0.000125)
867. feature "SEPTIN11" (0.000125)
868. feature "PPM1K" (0.000124)
869. feature "NCBP3" (0.000124)
870. feature "TNFSF13B" (0.000124)
871. feature "DRAP1" (0.000124)
872. feature "RASSF3" (0.000124)
873. feature "CIRBP" (0.000124)
874. feature "BOLA3" (0.000124)
875. feature "FKBP5" (0.000123)
876. feature "SRI" (0.000123)
877. feature "RRP1B" (0.000123)
878. feature "DHX37" (0.000122)
879. feature "CALB1" (0.000122)
880. feature "TINAGL1" (0.000122)
881. feature "H2AX" (0.000122)
882. feature "KEAP1" (0.000122)
883. feature "ELOA" (0.000122)
884. feature "INHBA" (0.000122)
885. feature "LINC02762" (0.000122)
886. feature "PSME4" (0.000121)
887. feature "BCAR1" (0.000121)
888. feature "ZC3H7A" (0.000121)
889. feature "ATXN2L" (0.000121)
890. feature "EIF2B4" (0.000121)
891. feature "RNF10" (0.000121)
892. feature "RAB5B" (0.000121)
893. feature "MAP3K2" (0.000120)
894. feature "STUB1" (0.000120)
895. feature "ZNF318" (0.000120)
896. feature "MNS1" (0.000120)
897. feature "PTGR1" (0.000120)
898. feature "IWS1" (0.000120)
899. feature "SLMAP" (0.000120)
900. feature "VAT1" (0.000120)
901. feature "ARFGEF1" (0.000120)
902. feature "CEP78" (0.000119)
903. feature "GABPB2" (0.000119)
904. feature "SINHCAFP3" (0.000119)
905. feature "FGF12" (0.000119)
906. feature "UIMC1" (0.000119)
907. feature "GSS" (0.000119)
908. feature "INCENP" (0.000119)
909. feature "EMP2" (0.000119)
910. feature "ZNF146" (0.000119)
911. feature "PLCXD2" (0.000118)
912. feature "HIF3A" (0.000118)
913. feature "BMS1" (0.000118)
914. feature "PABPN1" (0.000118)
915. feature "AFF4" (0.000118)
916. feature "HOXB7" (0.000118)
917. feature "LPP" (0.000118)
918. feature "PCBD1" (0.000118)
919. feature "C2orf49" (0.000117)
920. feature "FAM83H" (0.000117)
921. feature "FARSA" (0.000117)
922. feature "ZNF326" (0.000117)
923. feature "MDC1" (0.000117)
924. feature "FOXP4" (0.000117)
925. feature "CERS2" (0.000117)
926. feature "UBE2C" (0.000116)
927. feature "MARK3" (0.000116)
928. feature "GMCL1" (0.000116)
929. feature "C12orf75" (0.000116)
930. feature "VEGFB" (0.000116)
931. feature "OXLD1" (0.000116)
932. feature "CCDC88A" (0.000115)
933. feature "DCTN2" (0.000115)
934. feature "MGMT" (0.000115)
935. feature "ARHGAP1" (0.000115)
936. feature "ADAM15" (0.000114)
937. feature "ALKBH5" (0.000114)
938. feature "PCYT1A" (0.000114)
939. feature "CD151" (0.000114)
940. feature "INO80E" (0.000114)
941. feature "TASOR" (0.000113)
942. feature "SHISA5" (0.000113)
943. feature "MOB3A" (0.000113)
944. feature "GIPC1" (0.000113)
945. feature "POLDIP2" (0.000113)
946. feature "PTP4A1" (0.000113)
947. feature "PA2G4P6" (0.000113)
948. feature "EPAS1" (0.000113)
949. feature "SETD3" (0.000112)
950. feature "TAF10" (0.000112)
951. feature "PSMD13" (0.000112)
952. feature "LINC01291" (0.000112)
953. feature "MAP3K4" (0.000111)
954. feature "NCOA1" (0.000111)
955. feature "MAGEF1" (0.000111)
956. feature "ILRUN" (0.000111)
957. feature "MCRIP2" (0.000111)
958. feature "SAC3D1" (0.000111)
959. feature "RPLP0P2" (0.000110)
960. feature "ING2" (0.000110)
961. feature "SCNM1" (0.000110)
962. feature "FAM199X" (0.000110)
963. feature "DNM2" (0.000110)
964. feature "NSD2" (0.000110)
965. feature "ANKRD40" (0.000109)
966. feature "MCL1" (0.000109)
967. feature "TSR3" (0.000109)
968. feature "ERF" (0.000109)
969. feature "LCLAT1" (0.000109)
970. feature "C20orf27" (0.000109)
971. feature "CBX4" (0.000109)
972. feature "ZYX" (0.000109)
973. feature "YKT6" (0.000108)
974. feature "FAM214A" (0.000108)
975. feature "MSR1" (0.000108)
976. feature "PPP1R3G" (0.000108)
977. feature "BMPR1B" (0.000108)
978. feature "RPS19BP1" (0.000108)
979. feature "DCAF6" (0.000108)
980. feature "THOC7" (0.000108)
981. feature "MT-TA" (0.000108)
982. feature "SMDT1" (0.000107)
983. feature "CCDC92" (0.000107)
984. feature "DCAF7" (0.000107)
985. feature "BICD1" (0.000107)
986. feature "CDK5RAP2" (0.000106)
987. feature "GALNT1" (0.000106)
988. feature "DGKZ" (0.000106)
989. feature "CYB5R3" (0.000106)
990. feature "ZBTB45" (0.000106)
991. feature "RAD51" (0.000106)
992. feature "PAPSS2" (0.000106)
993. feature "SH3RF1" (0.000106)
994. feature "ARIH1" (0.000106)
995. feature "CDK12" (0.000106)
996. feature "MBD6" (0.000105)
997. feature "MAST4" (0.000105)
998. feature "SENP5" (0.000105)
999. feature "TUBA1C" (0.000105)
1000. feature "RIPK2" (0.000105)
1001. feature "FMR1" (0.000105)
1002. feature "PI4KB" (0.000105)
1003. feature "SEC61G" (0.000105)
1004. feature "DGUOK" (0.000105)
1005. feature "MAPK14" (0.000104)
1006. feature "HIRIP3" (0.000104)
1007. feature "DHTKD1" (0.000104)
1008. feature "RFK" (0.000104)
1009. feature "AAMP" (0.000103)
1010. feature "NCKAP1" (0.000103)
1011. feature "KCTD20" (0.000103)
1012. feature "PTGES2" (0.000103)
1013. feature "MINK1" (0.000103)
1014. feature "CDA" (0.000102)
1015. feature "RPSAP48" (0.000102)
1016. feature "SCP2" (0.000102)
1017. feature "GALM" (0.000102)
1018. feature "SLC25A1" (0.000102)
1019. feature "NEK2" (0.000102)
1020. feature "KHSRP" (0.000101)
1021. feature "ZBTB38" (0.000101)
1022. feature "GRN" (0.000101)
1023. feature "YTHDF3" (0.000101)
1024. feature "TAF3" (0.000101)
1025. feature "PITX1" (0.000101)
1026. feature "IQCE" (0.000101)
1027. feature "SUSD6" (0.000101)
1028. feature "PSIP1" (0.000101)
1029. feature "CFAP97" (0.000101)
1030. feature "GDF15" (0.000100)
1031. feature "LARP1B" (0.000100)
1032. feature "STK11" (0.000100)
1033. feature "CHP1" (0.000100)
1034. feature "SELENOH" (0.000100)
1035. feature "UCA1" (0.000100)
1036. feature "LDLRAP1" (0.000100)
1037. feature "KRT81" (0.000100)
1038. feature "KCNK6" (0.000100)
1039. feature "PHC3" (0.000099)
1040. feature "MYEF2" (0.000099)
1041. feature "ZDHHC6" (0.000099)
1042. feature "CCDC26" (0.000099)
1043. feature "HIPK2" (0.000099)
1044. feature "MTMR12" (0.000099)
1045. feature "PANK3" (0.000099)
1046. feature "POGK" (0.000099)
1047. feature "MLLT6" (0.000098)
1048. feature "WDR54" (0.000098)
1049. feature "WDR82" (0.000098)
1050. feature "SPRR1B" (0.000098)
1051. feature "CNOT2" (0.000098)
1052. feature "SLC35C2" (0.000098)
1053. feature "MIR210HG" (0.000098)
1054. feature "EPS8L2" (0.000098)
1055. feature "LSM14A" (0.000098)
1056. feature "RHBDD2" (0.000098)
1057. feature "KCTD3" (0.000098)
1058. feature "SMC5" (0.000097)
1059. feature "GDE1" (0.000097)
1060. feature "RNF4" (0.000097)
1061. feature "UBE2I" (0.000097)
1062. feature "DHX40" (0.000097)
1063. feature "PCF11" (0.000097)
1064. feature "ARFGEF3" (0.000097)
1065. feature "TNS4" (0.000097)
1066. feature "HMGCS1" (0.000097)
1067. feature "LYPD4" (0.000097)
1068. feature "NEDD1" (0.000097)
1069. feature "GNG5" (0.000097)
1070. feature "TMEM45B" (0.000096)
1071. feature "RAB1B" (0.000096)
1072. feature "OTULIN" (0.000096)
1073. feature "CA2" (0.000096)
1074. feature "MPHOSPH9" (0.000096)
1075. feature "FBXW2" (0.000096)
1076. feature "ARID1A" (0.000095)
1077. feature "MKNK2" (0.000095)
1078. feature "BRCC3" (0.000095)
1079. feature "SGO1" (0.000095)
1080. feature "NOM1" (0.000095)
1081. feature "MUC5B" (0.000094)
1082. feature "PPP2R2C" (0.000094)
1083. feature "BAG1" (0.000094)
1084. feature "ARAF" (0.000094)
1085. feature "PPARA" (0.000094)
1086. feature "RRM1" (0.000094)
1087. feature "PDE8A" (0.000094)
1088. feature "WWC3" (0.000094)
1089. feature "STT3A" (0.000093)
1090. feature "ETAA1" (0.000093)
1091. feature "RHNO1" (0.000093)
1092. feature "CREB3L2" (0.000093)
1093. feature "ZFAS1" (0.000093)
1094. feature "TNFRSF12A" (0.000093)
1095. feature "KLF6" (0.000093)
1096. feature "TMEM45A" (0.000093)
1097. feature "RAP1A" (0.000092)
1098. feature "SLC35A4" (0.000092)
1099. feature "DHX16" (0.000092)
1100. feature "ETNK1" (0.000092)
1101. feature "FAM126B" (0.000092)
1102. feature "MED29" (0.000092)
1103. feature "ZNF629" (0.000092)
1104. feature "NAB1" (0.000092)
1105. feature "IFI6" (0.000092)
1106. feature "SRSF1" (0.000092)
1107. feature "PIP4K2A" (0.000091)
1108. feature "GNL1" (0.000091)
1109. feature "ZNF512B" (0.000091)
1110. feature "CIP2A" (0.000091)
1111. feature "MAPK1IP1L" (0.000091)
1112. feature "CAV2" (0.000091)
1113. feature "PRR14L" (0.000091)
1114. feature "VMA21" (0.000091)
1115. feature "LINC02511" (0.000091)
1116. feature "NUDT14" (0.000091)
1117. feature "PARD3" (0.000091)
1118. feature "CEP250" (0.000091)
1119. feature "CDCA4" (0.000091)
1120. feature "MPP7" (0.000090)
1121. feature "POLE" (0.000090)
1122. feature "SOX11" (0.000090)
1123. feature "FRY" (0.000090)
1124. feature "ARHGAP12" (0.000090)
1125. feature "CHAMP1" (0.000090)
1126. feature "ANGPTL4" (0.000090)
1127. feature "SLC1A5" (0.000090)
1128. feature "PALB2" (0.000090)
1129. feature "UACA" (0.000090)
1130. feature "KIF16B" (0.000089)
1131. feature "OPA1" (0.000089)
1132. feature "PIAS2" (0.000089)
1133. feature "ARID1B" (0.000089)
1134. feature "TMEM164" (0.000089)
1135. feature "AATF" (0.000089)
1136. feature "KCTD11" (0.000089)
1137. feature "FBXL19" (0.000089)
1138. feature "TEX261" (0.000089)
1139. feature "EIF5AL1" (0.000089)
1140. feature "RAB5C" (0.000089)
1141. feature "CDT1" (0.000089)
1142. feature "KIF13A" (0.000089)
1143. feature "DAXX" (0.000088)
1144. feature "SNHG6" (0.000088)
1145. feature "CHMP1A" (0.000088)
1146. feature "EPN1" (0.000088)
1147. feature "RARG" (0.000088)
1148. feature "KPNA3" (0.000088)
1149. feature "MARVELD3" (0.000088)
1150. feature "HIP1" (0.000088)
1151. feature "REV1" (0.000088)
1152. feature "RBM14" (0.000087)
1153. feature "KLHL7" (0.000087)
1154. feature "PCDHGA10" (0.000087)
1155. feature "IFITM2" (0.000087)
1156. feature "METTL5" (0.000087)
1157. feature "KRT15" (0.000087)
1158. feature "JOSD2" (0.000087)
1159. feature "OPRM1" (0.000087)
1160. feature "TTC28" (0.000087)
1161. feature "TATDN2" (0.000087)
1162. feature "SAP130" (0.000087)
1163. feature "KLC2" (0.000086)
1164. feature "KDM1B" (0.000086)
1165. feature "SAPCD2" (0.000086)
1166. feature "SRA1" (0.000086)
1167. feature "ERBIN" (0.000086)
1168. feature "MXRA5" (0.000086)
1169. feature "DHX38" (0.000086)
1170. feature "MACC1" (0.000086)
1171. feature "MFN2" (0.000086)
1172. feature "DLD" (0.000085)
1173. feature "EIPR1" (0.000085)
1174. feature "MYO9A" (0.000085)
1175. feature "PCBP1" (0.000085)
1176. feature "PSMD3" (0.000085)
1177. feature "PYCARD" (0.000085)
1178. feature "FAM114A1" (0.000085)
1179. feature "LAD1" (0.000085)
1180. feature "RPGRIP1L" (0.000085)
1181. feature "SCYL2" (0.000085)
1182. feature "EFCAB2" (0.000085)
1183. feature "DEDD" (0.000085)
1184. feature "TIAL1" (0.000085)
1185. feature "ZC3H4" (0.000085)
1186. feature "SMIM27" (0.000084)
1187. feature "TMEM52B" (0.000084)
1188. feature "PLEKHA4" (0.000084)
1189. feature "FLOT1" (0.000084)
1190. feature "METRNL" (0.000084)
1191. feature "DIO2" (0.000084)
1192. feature "BIK" (0.000084)
1193. feature "TCHP" (0.000083)
1194. feature "SYTL4" (0.000083)
1195. feature "CLOCK" (0.000083)
1196. feature "EPC2" (0.000083)
1197. feature "RAB3GAP1" (0.000083)
1198. feature "MED25" (0.000083)
1199. feature "MT-TS2" (0.000083)
1200. feature "ERRFI1" (0.000083)
1201. feature "ZBTB10" (0.000083)
1202. feature "GADD45B" (0.000083)
1203. feature "PPIH" (0.000083)
1204. feature "PLCB4" (0.000083)
1205. feature "EID1" (0.000083)
1206. feature "NACC2" (0.000083)
1207. feature "EFNB2" (0.000083)
1208. feature "FNIP1" (0.000083)
1209. feature "PSMD5" (0.000083)
1210. feature "MAGI2-AS3" (0.000083)
1211. feature "GSR" (0.000082)
1212. feature "THEM4" (0.000082)
1213. feature "VPS4A" (0.000082)
1214. feature "SMIM1" (0.000082)
1215. feature "ZNF511" (0.000082)
1216. feature "NSMF" (0.000082)
1217. feature "SNAPC1" (0.000082)
1218. feature "CCNQ" (0.000082)
1219. feature "HMGB1P20" (0.000082)
1220. feature "NDUFC1" (0.000082)
1221. feature "ZNF385A" (0.000082)
1222. feature "SPDEF" (0.000082)
1223. feature "KCTD15" (0.000081)
1224. feature "POLR3A" (0.000081)
1225. feature "NECTIN1" (0.000081)
1226. feature "LINC00205" (0.000081)
1227. feature "LAMTOR2" (0.000081)
1228. feature "PRKX" (0.000081)
1229. feature "MELK" (0.000081)
1230. feature "CS" (0.000081)
1231. feature "AVPI1" (0.000081)
1232. feature "AMFR" (0.000081)
1233. feature "SRP68" (0.000081)
1234. feature "ROGDI" (0.000081)
1235. feature "ANXA3" (0.000080)
1236. feature "LRIF1" (0.000080)
1237. feature "CACNG4" (0.000080)
1238. feature "PGM1" (0.000080)
1239. feature "ANKRD26" (0.000080)
1240. feature "KCNJ15" (0.000080)
1241. feature "ENO2" (0.000080)
1242. feature "ARID3A" (0.000080)
1243. feature "ZFAND2A" (0.000080)
1244. feature "SCYL3" (0.000080)
1245. feature "WDR5" (0.000080)
1246. feature "FAM102B" (0.000080)
1247. feature "DVL3" (0.000080)
1248. feature "DBT" (0.000080)
1249. feature "NOP10" (0.000080)
1250. feature "PPP2R1A" (0.000079)
1251. feature "PPL" (0.000079)
1252. feature "MRPS6" (0.000079)
1253. feature "ARMC6" (0.000079)
1254. feature "RNF130" (0.000079)
1255. feature "ASNSD1" (0.000079)
1256. feature "IFT74" (0.000079)
1257. feature "AGRN" (0.000079)
1258. feature "NUMA1" (0.000079)
1259. feature "SYCP2" (0.000079)
1260. feature "DUSP23" (0.000078)
1261. feature "TOLLIP" (0.000078)
1262. feature "OTUD7B" (0.000078)
1263. feature "STRN4" (0.000078)
1264. feature "PRPF31" (0.000078)
1265. feature "RIMKLA" (0.000078)
1266. feature "LINC01234" (0.000078)
1267. feature "IPO4" (0.000078)
1268. feature "UNC45A" (0.000078)
1269. feature "SFMBT1" (0.000078)
1270. feature "DROSHA" (0.000078)
1271. feature "RAB29" (0.000078)
1272. feature "RIN2" (0.000078)
1273. feature "ATF5" (0.000078)
1274. feature "SLC25A5" (0.000078)
1275. feature "MIEN1" (0.000077)
1276. feature "GYG1" (0.000077)
1277. feature "MARK1" (0.000077)
1278. feature "PTOV1" (0.000077)
1279. feature "TBC1D9" (0.000077)
1280. feature "TMEM184C" (0.000077)
1281. feature "MAEA" (0.000077)
1282. feature "MTUS1" (0.000077)
1283. feature "CNOT3" (0.000077)
1284. feature "RECQL4" (0.000077)
1285. feature "OSBPL3" (0.000077)
1286. feature "FAM76B" (0.000077)
1287. feature "DKK1" (0.000077)
1288. feature "MCM5" (0.000077)
1289. feature "AFAP1L2" (0.000077)
1290. feature "CLK2" (0.000077)
1291. feature "GRK2" (0.000077)
1292. feature "ZCCHC2" (0.000077)
1293. feature "TACC2" (0.000076)
1294. feature "EIF1AD" (0.000076)
1295. feature "ADARB1" (0.000076)
1296. feature "SPG21" (0.000076)
1297. feature "PPP1R21" (0.000076)
1298. feature "AGAP1" (0.000076)
1299. feature "FLAD1" (0.000076)
1300. feature "IPO9" (0.000076)
1301. feature "MAP2K3" (0.000076)
1302. feature "GNAI2" (0.000076)
1303. feature "GDAP2" (0.000076)
1304. feature "TAF9B" (0.000076)
1305. feature "NCAM1" (0.000076)
1306. feature "KRT4" (0.000076)
1307. feature "CSKMT" (0.000076)
1308. feature "FBRSL1" (0.000076)
1309. feature "SNAPC5" (0.000076)
1310. feature "AZI2" (0.000076)
1311. feature "MYB" (0.000076)
1312. feature "TMSB4XP4" (0.000075)
1313. feature "NUP93" (0.000075)
1314. feature "ARHGAP11A" (0.000075)
1315. feature "RGS10" (0.000075)
1316. feature "NACA4P" (0.000075)
1317. feature "ZNF85" (0.000075)
1318. feature "SECISBP2L" (0.000075)
1319. feature "DOCK10" (0.000075)
1320. feature "PATJ" (0.000075)
1321. feature "PMVK" (0.000075)
1322. feature "CXCL12" (0.000075)
1323. feature "PPP1R13L" (0.000075)
1324. feature "MRFAP1" (0.000075)
1325. feature "HGH1" (0.000075)
1326. feature "BAIAP2-DT" (0.000074)
1327. feature "PLCD3" (0.000074)
1328. feature "ITPR3" (0.000074)
1329. feature "ZHX3" (0.000074)
1330. feature "STX3" (0.000074)
1331. feature "KLHL8" (0.000074)
1332. feature "ARHGAP21" (0.000074)
1333. feature "REXO1" (0.000074)
1334. feature "MAP7D1" (0.000074)
1335. feature "RAB5A" (0.000074)
1336. feature "RFWD3" (0.000074)
1337. feature "PIN4" (0.000073)
1338. feature "BRWD3" (0.000073)
1339. feature "GLE1" (0.000073)
1340. feature "FAM13B" (0.000073)
1341. feature "SERINC5" (0.000073)
1342. feature "CMPK1" (0.000073)
1343. feature "NEK1" (0.000073)
1344. feature "RAB31" (0.000073)
1345. feature "NPY1R" (0.000073)
1346. feature "ZNF580" (0.000073)
1347. feature "PLSCR1" (0.000073)
1348. feature "BRMS1" (0.000073)
1349. feature "CTSL" (0.000073)
1350. feature "VCPIP1" (0.000072)
1351. feature "MYADM" (0.000072)
1352. feature "PGLS" (0.000072)
1353. feature "TYW3" (0.000072)
1354. feature "MKNK1" (0.000072)
1355. feature "TRIM7" (0.000072)
1356. feature "SLC2A4RG" (0.000072)
1357. feature "TRAF3IP1" (0.000072)
1358. feature "DCTN5" (0.000072)
1359. feature "PKP3" (0.000072)
1360. feature "TIAM1" (0.000072)
1361. feature "C11orf24" (0.000072)
1362. feature "SRFBP1" (0.000072)
1363. feature "IQCG" (0.000072)
1364. feature "ZNF26" (0.000072)
1365. feature "NDUFB4" (0.000072)
1366. feature "AFF1" (0.000072)
1367. feature "ELP3" (0.000072)
1368. feature "UBQLN4" (0.000071)
1369. feature "UPK1B" (0.000071)
1370. feature "PCGF3" (0.000071)
1371. feature "RUSC1" (0.000071)
1372. feature "BCL3" (0.000071)
1373. feature "HNRNPUL2" (0.000071)
1374. feature "FAM189B" (0.000071)
1375. feature "CASK" (0.000071)
1376. feature "CDC42EP1" (0.000071)
1377. feature "C1orf43" (0.000071)
1378. feature "ZNF268" (0.000070)
1379. feature "USP53" (0.000070)
1380. feature "TRIP13" (0.000070)
1381. feature "CARM1" (0.000070)
1382. feature "PMPCA" (0.000070)
1383. feature "WIPI2" (0.000070)
1384. feature "RAB3D" (0.000070)
1385. feature "KHNYN" (0.000070)
1386. feature "LIMS1" (0.000070)
1387. feature "BACH1" (0.000070)
1388. feature "B3GAT2" (0.000070)
1389. feature "MRPL45" (0.000070)
1390. feature "SPATA33" (0.000070)
1391. feature "MCM6" (0.000070)
1392. feature "NFX1" (0.000070)
1393. feature "YEATS4" (0.000070)
1394. feature "NCLN" (0.000070)
1395. feature "DLG5" (0.000070)
1396. feature "GADD45A" (0.000070)
1397. feature "UBE2E1" (0.000069)
1398. feature "LCMT1" (0.000069)
1399. feature "BRK1" (0.000069)
1400. feature "EHBP1L1" (0.000069)
1401. feature "PLAAT3" (0.000069)
1402. feature "PLEKHF2" (0.000069)
1403. feature "HES4" (0.000069)
1404. feature "DNPH1" (0.000069)
1405. feature "ORC4" (0.000069)
1406. feature "POLD2" (0.000069)
1407. feature "SPATA20" (0.000069)
1408. feature "BRPF3" (0.000069)
1409. feature "PITPNA" (0.000069)
1410. feature "PPP1R3D" (0.000069)
1411. feature "NECAB3" (0.000069)
1412. feature "RESF1" (0.000068)
1413. feature "TBC1D20" (0.000068)
1414. feature "AKR1A1" (0.000068)
1415. feature "WAC-AS1" (0.000068)
1416. feature "MGLL" (0.000068)
1417. feature "CEP128" (0.000068)
1418. feature "BTBD2" (0.000068)
1419. feature "POLR2F" (0.000068)
1420. feature "TOMM5" (0.000068)
1421. feature "GCLM" (0.000068)
1422. feature "TTC19" (0.000068)
1423. feature "MIPOL1" (0.000068)
1424. feature "CHTOP" (0.000068)
1425. feature "MAD2L2" (0.000067)
1426. feature "ADORA2B" (0.000067)
1427. feature "TNKS1BP1" (0.000067)
1428. feature "CHN1" (0.000067)
1429. feature "C1orf21" (0.000067)
1430. feature "CHAF1A" (0.000067)
1431. feature "FOXJ2" (0.000067)
1432. feature "PLAC8" (0.000067)
1433. feature "ZDHHC7" (0.000067)
1434. feature "BMPR2" (0.000067)
1435. feature "TARDBP" (0.000067)
1436. feature "YARS1" (0.000067)
1437. feature "SPINT1-AS1" (0.000067)
1438. feature "CLDN11" (0.000066)
1439. feature "SLC26A4-AS1" (0.000066)
1440. feature "RNF187" (0.000066)
1441. feature "PFKL" (0.000066)
1442. feature "CCP110" (0.000066)
1443. feature "HERC1" (0.000066)
1444. feature "COG4" (0.000066)
1445. feature "PHKG2" (0.000066)
1446. feature "CAMSAP1" (0.000066)
1447. feature "MCM2" (0.000066)
1448. feature "ZFHX3" (0.000065)
1449. feature "CBFA2T3" (0.000065)
1450. feature "VRK2" (0.000065)
1451. feature "HOOK1" (0.000065)
1452. feature "TMEM70" (0.000065)
1453. feature "MAILR" (0.000065)
1454. feature "MYO1C" (0.000065)
1455. feature "ADAMTS19" (0.000065)
1456. feature "DPM3" (0.000065)
1457. feature "FARP1" (0.000065)
1458. feature "C5orf51" (0.000065)
1459. feature "QRSL1" (0.000065)
1460. feature "SNCG" (0.000065)
1461. feature "KLC1" (0.000065)
1462. feature "JUND" (0.000065)
1463. feature "WWC1" (0.000065)
1464. feature "FBRS" (0.000065)
1465. feature "IER5" (0.000065)
1466. feature "COMT" (0.000064)
1467. feature "ARID4A" (0.000064)
1468. feature "PPP2R5D" (0.000064)
1469. feature "SIX4" (0.000064)
1470. feature "ZBTB2" (0.000064)
1471. feature "SIAH2" (0.000064)
1472. feature "CDR2L" (0.000064)
1473. feature "FUOM" (0.000064)
1474. feature "ATP13A2" (0.000064)
1475. feature "E2F4" (0.000064)
1476. feature "RC3H2" (0.000064)
1477. feature "ABL2" (0.000064)
1478. feature "UBE2J1" (0.000064)
1479. feature "EXOC7" (0.000064)
1480. feature "ZNF354A" (0.000064)
1481. feature "XRCC2" (0.000064)
1482. feature "CTBP1" (0.000064)
1483. feature "GRIPAP1" (0.000063)
1484. feature "SMG5" (0.000063)
1485. feature "SLC48A1" (0.000063)
1486. feature "FAM181B" (0.000063)
1487. feature "XPNPEP1" (0.000063)
1488. feature "SCCPDH" (0.000063)
1489. feature "POLK" (0.000063)
1490. feature "MDFI" (0.000063)
1491. feature "ZNF165" (0.000063)
1492. feature "TNFAIP8L1" (0.000063)
1493. feature "SLC2A11" (0.000063)
1494. feature "TGFB3" (0.000063)
1495. feature "RAB27A" (0.000063)
1496. feature "AKR7A2" (0.000063)
1497. feature "COL7A1" (0.000063)
1498. feature "CEP120" (0.000063)
1499. feature "SLC52A2" (0.000063)
1500. feature "ZNF252P" (0.000063)
1501. feature "ZADH2" (0.000063)
1502. feature "GMPPA" (0.000063)
1503. feature "DGKD" (0.000062)
1504. feature "PSMA5" (0.000062)
1505. feature "AFAP1" (0.000062)
1506. feature "MAB21L3" (0.000062)
1507. feature "MLXIP" (0.000062)
1508. feature "ZNF8-DT" (0.000062)
1509. feature "ABT1" (0.000062)
1510. feature "DYNC2I1" (0.000062)
1511. feature "SDC1" (0.000062)
1512. feature "USP6NL" (0.000062)
1513. feature "PCDHB1" (0.000062)
1514. feature "IER3IP1" (0.000062)
1515. feature "LTBR" (0.000062)
1516. feature "EFCAB14" (0.000062)
1517. feature "RAB2B" (0.000062)
1518. feature "AP1S3" (0.000062)
1519. feature "AXIN1" (0.000062)
1520. feature "HMGB1P8" (0.000062)
1521. feature "SPP1" (0.000062)
1522. feature "PIK3CB" (0.000061)
1523. feature "PLCE1" (0.000061)
1524. feature "WDR26" (0.000061)
1525. feature "DDIAS" (0.000061)
1526. feature "PCCA-DT" (0.000061)
1527. feature "FAM210B" (0.000061)
1528. feature "DNAJA3" (0.000061)
1529. feature "MAPK9" (0.000061)
1530. feature "MED1" (0.000061)
1531. feature "CPEB1" (0.000061)
1532. feature "CREB1" (0.000061)
1533. feature "SSNA1" (0.000061)
1534. feature "FOXO3" (0.000061)
1535. feature "SLC19A1" (0.000061)
1536. feature "DIP2C" (0.000061)
1537. feature "ZNF358" (0.000061)
1538. feature "DUS4L" (0.000061)
1539. feature "BACE2" (0.000060)
1540. feature "NKAPD1" (0.000060)
1541. feature "ZNF91" (0.000060)
1542. feature "MARVELD1" (0.000060)
1543. feature "CCDC57" (0.000060)
1544. feature "HOMER2" (0.000060)
1545. feature "IER5L" (0.000060)
1546. feature "MSMB" (0.000060)
1547. feature "SOS2" (0.000060)
1548. feature "TMCC1" (0.000060)
1549. feature "UHRF1BP1" (0.000060)
1550. feature "AGO1" (0.000060)
1551. feature "NSMCE4A" (0.000060)
1552. feature "ZMYM3" (0.000060)
1553. feature "FOXC1" (0.000060)
1554. feature "ZNF280B" (0.000060)
1555. feature "PUSL1" (0.000060)
1556. feature "APEH" (0.000060)
1557. feature "BRMS1L" (0.000060)
1558. feature "TNNT1" (0.000060)
1559. feature "NFKB1" (0.000060)
1560. feature "TST" (0.000060)
1561. feature "HAUS1" (0.000060)
1562. feature "BTBD6" (0.000059)
1563. feature "SPIRE1" (0.000059)
1564. feature "UBE2E3" (0.000059)
1565. feature "TCEAL3" (0.000059)
1566. feature "ASB8" (0.000059)
1567. feature "LIMK1" (0.000059)
1568. feature "FBXO42" (0.000059)
1569. feature "MT-TD" (0.000059)
1570. feature "LYPLA2" (0.000059)
1571. feature "YJEFN3" (0.000059)
1572. feature "KLF9" (0.000059)
1573. feature "PCIF1" (0.000059)
1574. feature "TUBB6" (0.000059)
1575. feature "TMEM80" (0.000059)
1576. feature "CHCHD7" (0.000059)
1577. feature "LDHB" (0.000059)
1578. feature "CENPM" (0.000059)
1579. feature "SELENOO" (0.000059)
1580. feature "CAMKK2" (0.000059)
1581. feature "PLK1" (0.000059)
1582. feature "TEDC2-AS1" (0.000059)
1583. feature "TRIB3" (0.000059)
1584. feature "GMEB2" (0.000059)
1585. feature "SNX4" (0.000059)
1586. feature "ARHGAP23" (0.000059)
1587. feature "AJAP1" (0.000058)
1588. feature "DCLRE1B" (0.000058)
1589. feature "WDR46" (0.000058)
1590. feature "ENOX2" (0.000058)
1591. feature "CCNC" (0.000058)
1592. feature "DANT1" (0.000058)
1593. feature "SIPA1L1" (0.000058)
1594. feature "PET117" (0.000058)
1595. feature "FASTKD3" (0.000058)
1596. feature "ARHGEF7" (0.000058)
1597. feature "EVPL" (0.000058)
1598. feature "POLD1" (0.000058)
1599. feature "TCF3" (0.000058)
1600. feature "NOL4L" (0.000058)
1601. feature "FAM193B" (0.000058)
1602. feature "PHACTR1" (0.000058)
1603. feature "ARAP1" (0.000058)
1604. feature "NGRN" (0.000058)
1605. feature "CACNB3" (0.000058)
1606. feature "RNASEH1" (0.000058)
1607. feature "SREBF1" (0.000058)
1608. feature "RNF32" (0.000058)
1609. feature "TSSC4" (0.000057)
1610. feature "ZFYVE9" (0.000057)
1611. feature "PKN1" (0.000057)
1612. feature "TAF12" (0.000057)
1613. feature "HCG18" (0.000057)
1614. feature "C2orf68" (0.000057)
1615. feature "RIPK1" (0.000057)
1616. feature "C1orf115" (0.000057)
1617. feature "LCORL" (0.000057)
1618. feature "CAD" (0.000057)
1619. feature "IGFBP7" (0.000057)
1620. feature "SGTA" (0.000057)
1621. feature "SSX2IP" (0.000057)
1622. feature "SMIM30" (0.000057)
1623. feature "WDR18" (0.000057)
1624. feature "ECH1" (0.000057)
1625. feature "HMG20B" (0.000057)
1626. feature "MCRIP1" (0.000057)
1627. feature "CRPPA" (0.000057)
1628. feature "GNPAT" (0.000057)
1629. feature "RAB30" (0.000057)
1630. feature "VARS1" (0.000057)
1631. feature "SPRYD4" (0.000057)
1632. feature "GIT1" (0.000057)
1633. feature "RFC5" (0.000057)
1634. feature "CDK9" (0.000056)
1635. feature "TRMT2A" (0.000056)
1636. feature "MIR99AHG" (0.000056)
1637. feature "PRDM4" (0.000056)
1638. feature "BIRC5" (0.000056)
1639. feature "MT-TC" (0.000056)
1640. feature "SH3BGRL" (0.000056)
1641. feature "VPS26B" (0.000056)
1642. feature "PYCR1" (0.000056)
1643. feature "ABLIM1" (0.000056)
1644. feature "SLAIN2" (0.000056)
1645. feature "SURF1" (0.000056)
1646. feature "L2HGDH" (0.000056)
1647. feature "PACSIN3" (0.000056)
1648. feature "CRBN" (0.000056)
1649. feature "PRPSAP2" (0.000056)
1650. feature "KDM4B" (0.000056)
1651. feature "SCARB1" (0.000056)
1652. feature "SPAG1" (0.000056)
1653. feature "KIF9" (0.000056)
1654. feature "CLEC4OP" (0.000056)
1655. feature "HS6ST1" (0.000056)
1656. feature "KLLN" (0.000056)
1657. feature "ARHGAP17" (0.000056)
1658. feature "AKT2" (0.000056)
1659. feature "ALDOA" (0.000056)
1660. feature "SLC25A51" (0.000055)
1661. feature "CSNK2A2" (0.000055)
1662. feature "SLC25A28" (0.000055)
1663. feature "PFKFB4" (0.000055)
1664. feature "PSMG1" (0.000055)
1665. feature "FOXRED2" (0.000055)
1666. feature "FADS3" (0.000055)
1667. feature "HOXC13" (0.000055)
1668. feature "UBL4A" (0.000055)
1669. feature "BRF1" (0.000055)
1670. feature "CYP1A1" (0.000055)
1671. feature "MGAT4A" (0.000055)
1672. feature "CNDP2" (0.000055)
1673. feature "ANKMY2" (0.000055)
1674. feature "MLLT10" (0.000055)
1675. feature "ZZZ3" (0.000055)
1676. feature "S1PR2" (0.000055)
1677. feature "CCS" (0.000055)
1678. feature "TP53INP1" (0.000055)
1679. feature "FBXO30" (0.000055)
1680. feature "KXD1" (0.000054)
1681. feature "FNTA" (0.000054)
1682. feature "MROH1" (0.000054)
1683. feature "TMEM256" (0.000054)
1684. feature "NEIL3" (0.000054)
1685. feature "C5orf38" (0.000054)
1686. feature "ZNF574" (0.000054)
1687. feature "IRS2" (0.000054)
1688. feature "BICD2" (0.000054)
1689. feature "LMX1B" (0.000054)
1690. feature "TBC1D10A" (0.000054)
1691. feature "ZDHHC12" (0.000054)
1692. feature "ZNF398" (0.000054)
1693. feature "BTRC" (0.000054)
1694. feature "SNORD3B-1" (0.000054)
1695. feature "LAMA5" (0.000054)
1696. feature "HSPA2" (0.000054)
1697. feature "CKB" (0.000054)
1698. feature "COL6A3" (0.000054)
1699. feature "BEND6" (0.000054)
1700. feature "RRP12" (0.000054)
1701. feature "SETD1B" (0.000054)
1702. feature "TEAD1" (0.000054)
1703. feature "MT-TN" (0.000054)
1704. feature "SRF" (0.000054)
1705. feature "ZBTB34" (0.000054)
1706. feature "LRRC45" (0.000054)
1707. feature "ARHGEF28" (0.000054)
1708. feature "SHKBP1" (0.000053)
1709. feature "PHRF1" (0.000053)
1710. feature "OTUD4" (0.000053)
1711. feature "CKS1B" (0.000053)
1712. feature "SDHAF2" (0.000053)
1713. feature "ITSN2" (0.000053)
1714. feature "SORBS3" (0.000053)
1715. feature "AP2A1" (0.000053)
1716. feature "DOHH" (0.000053)
1717. feature "ABL1" (0.000053)
1718. feature "RAI14" (0.000053)
1719. feature "ZNFX1" (0.000053)
1720. feature "AKAP5" (0.000053)
1721. feature "FCHO2" (0.000053)
1722. feature "TPGS1" (0.000053)
1723. feature "RNF126" (0.000053)
1724. feature "NPRL3" (0.000053)
1725. feature "NF2" (0.000053)
1726. feature "SAMD12" (0.000053)
1727. feature "TCF7L1" (0.000053)
1728. feature "SF3B4" (0.000053)
1729. feature "SOX15" (0.000053)
1730. feature "NCK1-DT" (0.000053)
1731. feature "CPTP" (0.000053)
1732. feature "SMKR1" (0.000052)
1733. feature "DCP1A" (0.000052)
1734. feature "PIGS" (0.000052)
1735. feature "PPP1R37" (0.000052)
1736. feature "CABLES1" (0.000052)
1737. feature "CTPS2" (0.000052)
1738. feature "ID3" (0.000052)
1739. feature "USP38" (0.000052)
1740. feature "NOL3" (0.000052)
1741. feature "MAP3K14" (0.000052)
1742. feature "ZNF598" (0.000052)
1743. feature "KSR2" (0.000052)
1744. feature "GTPBP6" (0.000052)
1745. feature "TTYH3" (0.000052)
1746. feature "PAK4" (0.000052)
1747. feature "PFKFB2" (0.000052)
1748. feature "SETBP1" (0.000052)
1749. feature "WBP2" (0.000052)
1750. feature "MGAT4B" (0.000052)
1751. feature "UBXN2B" (0.000052)
1752. feature "KCTD2" (0.000052)
1753. feature "RGL2" (0.000052)
1754. feature "CEP135" (0.000052)
1755. feature "TFRC" (0.000052)
1756. feature "TPRA1" (0.000052)
1757. feature "WT1-AS" (0.000052)
1758. feature "TBL1X" (0.000052)
1759. feature "VPS54" (0.000052)
1760. feature "MAP2" (0.000051)
1761. feature "ANKRD39" (0.000051)
1762. feature "FANCE" (0.000051)
1763. feature "POLE2" (0.000051)
1764. feature "PCNX2" (0.000051)
1765. feature "MINDY2" (0.000051)
1766. feature "BNC1" (0.000051)
1767. feature "ZBTB37" (0.000051)
1768. feature "SPA17" (0.000051)
1769. feature "TMEM250" (0.000051)
1770. feature "HHLA3" (0.000051)
1771. feature "METTL22" (0.000051)
1772. feature "CCDC8" (0.000051)
1773. feature "ERI1" (0.000051)
1774. feature "RHBDL2" (0.000051)
1775. feature "TMEM259" (0.000051)
1776. feature "NR1H2" (0.000051)
1777. feature "CLDN4" (0.000051)
1778. feature "FARP2" (0.000051)
1779. feature "CANT1" (0.000051)
1780. feature "PLA2G12A" (0.000050)
1781. feature "L3MBTL2" (0.000050)
1782. feature "YAF2" (0.000050)
1783. feature "HPCAL1" (0.000050)
1784. feature "ARL2" (0.000050)
1785. feature "SIRT1" (0.000050)
1786. feature "AKAP17A" (0.000050)
1787. feature "ARNTL2" (0.000050)
1788. feature "NCOA2" (0.000050)
1789. feature "KMT2B" (0.000050)
1790. feature "FBXO11" (0.000050)
1791. feature "C8orf82" (0.000050)
1792. feature "GCLC" (0.000050)
1793. feature "BOP1" (0.000050)
1794. feature "COL5A1" (0.000050)
1795. feature "NCK1" (0.000050)
1796. feature "SAMD15" (0.000050)
1797. feature "PTPN13" (0.000050)
1798. feature "WDR77" (0.000050)
1799. feature "SENP3" (0.000050)
1800. feature "SSH3" (0.000050)
1801. feature "MBD3" (0.000050)
1802. feature "HOMER3" (0.000050)
1803. feature "DCAF10" (0.000050)
1804. feature "ZNF483" (0.000049)
1805. feature "R3HCC1L" (0.000049)
1806. feature "TCEAL1" (0.000049)
1807. feature "NPLOC4" (0.000049)
1808. feature "CHTF18" (0.000049)
1809. feature "MLX" (0.000049)
1810. feature "DBNDD1" (0.000049)
1811. feature "CNTROB" (0.000049)
1812. feature "ALDOC" (0.000049)
1813. feature "ADAT1" (0.000049)
1814. feature "ZNF628" (0.000049)
1815. feature "PPP2R5A" (0.000049)
1816. feature "RNF220" (0.000049)
1817. feature "PCYT1B" (0.000049)
1818. feature "TRIM41" (0.000049)
1819. feature "SNX24" (0.000049)
1820. feature "ESS2" (0.000049)
1821. feature "EZH1" (0.000049)
1822. feature "RRN3P3" (0.000049)
1823. feature "PATL1" (0.000049)
1824. feature "LSM10" (0.000049)
1825. feature "NLE1" (0.000049)
1826. feature "EGR3" (0.000049)
1827. feature "GMFB" (0.000048)
1828. feature "ZNF362" (0.000048)
1829. feature "TOP1MT" (0.000048)
1830. feature "SCAF8" (0.000048)
1831. feature "ZNF142" (0.000048)
1832. feature "SCPEP1" (0.000048)
1833. feature "ZNF777" (0.000048)
1834. feature "DRAM2" (0.000048)
1835. feature "PAN3" (0.000048)
1836. feature "PSMG3-AS1" (0.000048)
1837. feature "TET3" (0.000048)
1838. feature "TONSL" (0.000048)
1839. feature "BCL2L12" (0.000048)
1840. feature "MORC3" (0.000048)
1841. feature "LIAS" (0.000048)
1842. feature "C16orf91" (0.000048)
1843. feature "CUL4B" (0.000048)
1844. feature "CTSZ" (0.000048)
1845. feature "QSOX1" (0.000048)
1846. feature "PICALM" (0.000048)
1847. feature "POLR2H" (0.000048)
1848. feature "DYRK2" (0.000048)
1849. feature "AMOTL2" (0.000048)
1850. feature "ANXA4" (0.000048)
1851. feature "ACKR3" (0.000048)
1852. feature "P2RY2" (0.000048)
1853. feature "CIPC" (0.000048)
1854. feature "NAGK" (0.000048)
1855. feature "EFNB1" (0.000048)
1856. feature "CIAO2A" (0.000048)
1857. feature "ISY1" (0.000048)
1858. feature "PPM1A" (0.000047)
1859. feature "RNF223" (0.000047)
1860. feature "PATZ1" (0.000047)
1861. feature "CAMK2D" (0.000047)
1862. feature "CAMSAP3" (0.000047)
1863. feature "LINC02582" (0.000047)
1864. feature "CA9" (0.000047)
1865. feature "CRKL" (0.000047)
1866. feature "SSH1" (0.000047)
1867. feature "NUAK1" (0.000047)
1868. feature "HDAC7" (0.000047)
1869. feature "GTPBP2" (0.000047)
1870. feature "OGDH" (0.000047)
1871. feature "GABPA" (0.000047)
1872. feature "MNT" (0.000047)
1873. feature "RNF34" (0.000047)
1874. feature "ZNF605" (0.000047)
1875. feature "EFHD1" (0.000047)
1876. feature "SIPA1L2" (0.000047)
1877. feature "ZNF324" (0.000047)
1878. feature "DNAAF5" (0.000047)
1879. feature "SCAPER" (0.000047)
1880. feature "TMEM69" (0.000047)
1881. feature "AGAP3" (0.000047)
1882. feature "LYPD1" (0.000047)
1883. feature "SLC38A10" (0.000047)
1884. feature "AK7" (0.000047)
1885. feature "PNMA1" (0.000046)
1886. feature "CCND3" (0.000046)
1887. feature "TBC1D13" (0.000046)
1888. feature "TMEM40" (0.000046)
1889. feature "MUL1" (0.000046)
1890. feature "WFS1" (0.000046)
1891. feature "TRIM8" (0.000046)
1892. feature "IRX2" (0.000046)
1893. feature "RHOT2" (0.000046)
1894. feature "BRAT1" (0.000046)
1895. feature "ORAI1" (0.000046)
1896. feature "CASP6" (0.000046)
1897. feature "WDFY3" (0.000046)
1898. feature "JADE2" (0.000046)
1899. feature "TYMP" (0.000046)
1900. feature "CAPNS1" (0.000046)
1901. feature "GREM1" (0.000046)
1902. feature "SNAP47" (0.000046)
1903. feature "YY1AP1" (0.000046)
1904. feature "LINC01503" (0.000046)
1905. feature "IMP3" (0.000046)
1906. feature "NUP98" (0.000046)
1907. feature "RAB11FIP4" (0.000046)
1908. feature "ADGRG1" (0.000046)
1909. feature "NDUFA8" (0.000046)
1910. feature "FDFT1" (0.000046)
1911. feature "JMJD6" (0.000046)
1912. feature "FNDC10" (0.000046)
1913. feature "LMF2" (0.000046)
1914. feature "NEMP1" (0.000045)
1915. feature "GATC" (0.000045)
1916. feature "UBA3" (0.000045)
1917. feature "SP2" (0.000045)
1918. feature "SDHAF3" (0.000045)
1919. feature "STK39" (0.000045)
1920. feature "HAUS5" (0.000045)
1921. feature "FGD4" (0.000045)
1922. feature "ADM" (0.000045)
1923. feature "TIMM29" (0.000045)
1924. feature "USF1" (0.000045)
1925. feature "NIPSNAP3A" (0.000045)
1926. feature "TTLL5" (0.000045)
1927. feature "FRS2" (0.000045)
1928. feature "ZNF526" (0.000045)
1929. feature "FGFBP1" (0.000045)
1930. feature "ERVMER34-1" (0.000045)
1931. feature "SYK" (0.000045)
1932. feature "POLG" (0.000045)
1933. feature "PIP4P1" (0.000045)
1934. feature "PEA15" (0.000045)
1935. feature "TCAF1" (0.000045)
1936. feature "UBE2D1" (0.000044)
1937. feature "C22orf46" (0.000044)
1938. feature "TRAK1" (0.000044)
1939. feature "GFOD1" (0.000044)
1940. feature "DNAJC5" (0.000044)
1941. feature "PHF8" (0.000044)
1942. feature "GAS8" (0.000044)
1943. feature "SOCS2" (0.000044)
1944. feature "GPATCH1" (0.000044)
1945. feature "PPP4R1" (0.000044)
1946. feature "ABR" (0.000044)
1947. feature "TLE3" (0.000044)
1948. feature "SAMHD1" (0.000044)
1949. feature "USP25" (0.000044)
1950. feature "DERA" (0.000044)
1951. feature "CCNY" (0.000044)
1952. feature "KBTBD6" (0.000044)
1953. feature "SALL4" (0.000044)
1954. feature "PRPS1" (0.000044)
1955. feature "FAM83G" (0.000044)
1956. feature "RPL17" (0.000044)
1957. feature "MTMR2" (0.000044)
1958. feature "NXF1" (0.000044)
1959. feature "ZBED2" (0.000044)
1960. feature "CDK6" (0.000044)
1961. feature "EHD1" (0.000044)
1962. feature "LRRCC1" (0.000044)
1963. feature "SACS" (0.000044)
1964. feature "TP53BP2" (0.000044)
1965. feature "GATA2" (0.000044)
1966. feature "C6orf132" (0.000044)
1967. feature "C19orf47" (0.000044)
1968. feature "RTL6" (0.000044)
1969. feature "FAM104A" (0.000044)
1970. feature "LATS2" (0.000044)
1971. feature "NAALADL2" (0.000044)
1972. feature "MXD1" (0.000043)
1973. feature "LEMD3" (0.000043)
1974. feature "ACOT7" (0.000043)
1975. feature "RBAK" (0.000043)
1976. feature "TEX30" (0.000043)
1977. feature "HID1" (0.000043)
1978. feature "KAZN" (0.000043)
1979. feature "IRS1" (0.000043)
1980. feature "ZNF544" (0.000043)
1981. feature "WNT7B" (0.000043)
1982. feature "FYCO1" (0.000043)
1983. feature "TAF6" (0.000043)
1984. feature "CLIC4" (0.000043)
1985. feature "B3GALT6" (0.000043)
1986. feature "MPDU1" (0.000043)
1987. feature "ZBED4" (0.000043)
1988. feature "EHBP1" (0.000043)
1989. feature "TMEM184B" (0.000043)
1990. feature "TCHH" (0.000043)
1991. feature "MB" (0.000043)
1992. feature "HPRT1" (0.000043)
1993. feature "CRIP2" (0.000043)
1994. feature "FHL2" (0.000043)
1995. feature "CACNB2" (0.000043)
1996. feature "ZNF562" (0.000043)
1997. feature "MBNL3" (0.000043)
1998. feature "EP400P1" (0.000043)
1999. feature "FLOT2" (0.000043)
2000. feature "ZFAND2B" (0.000043)
2001. feature "SEPTIN8" (0.000043)
2002. feature "LINC02345" (0.000043)
2003. feature "OAZ2" (0.000043)
2004. feature "CNPPD1" (0.000043)
2005. feature "CHPF" (0.000043)
2006. feature "ZNF347" (0.000043)
2007. feature "ZRANB1" (0.000042)
2008. feature "PTPDC1" (0.000042)
2009. feature "GM2A" (0.000042)
2010. feature "PDE12" (0.000042)
2011. feature "CERS6" (0.000042)
2012. feature "SDHB" (0.000042)
2013. feature "GAS2L1" (0.000042)
2014. feature "PAPSS1" (0.000042)
2015. feature "ZNF467" (0.000042)
2016. feature "UBE2T" (0.000042)
2017. feature "C16orf72" (0.000042)
2018. feature "SIX3" (0.000042)
2019. feature "MIEF1" (0.000042)
2020. feature "LDOC1" (0.000042)
2021. feature "RHOV" (0.000042)
2022. feature "CMBL" (0.000042)
2023. feature "ZC3H12A" (0.000042)
2024. feature "NLK" (0.000042)
2025. feature "GRK6" (0.000042)
2026. feature "MREG" (0.000041)
2027. feature "DAZAP2" (0.000041)
2028. feature "ATXN1L" (0.000041)
2029. feature "MBD5" (0.000041)
2030. feature "MAP3K9" (0.000041)
2031. feature "TJP3" (0.000041)
2032. feature "UBE2F" (0.000041)
2033. feature "FRAT2" (0.000041)
2034. feature "MIXL1" (0.000041)
2035. feature "TCF19" (0.000041)
2036. feature "PXMP4" (0.000041)
2037. feature "DNAH11" (0.000041)
2038. feature "FAR1" (0.000041)
2039. feature "PLEKHG3" (0.000041)
2040. feature "MSL3P1" (0.000041)
2041. feature "PKMYT1" (0.000041)
2042. feature "CDIPT" (0.000041)
2043. feature "ABHD10" (0.000041)
2044. feature "CCDC88C" (0.000041)
2045. feature "CAPN10" (0.000041)
2046. feature "NPTN" (0.000041)
2047. feature "FAM83A-AS1" (0.000041)
2048. feature "OXNAD1" (0.000041)
2049. feature "KLHL20" (0.000041)
2050. feature "PITPNC1" (0.000041)
2051. feature "SFXN2" (0.000041)
2052. feature "PCNX3" (0.000041)
2053. feature "RAVER1" (0.000041)
2054. feature "INPPL1" (0.000041)
2055. feature "UBA6-DT" (0.000041)
2056. feature "RNPEPL1" (0.000040)
2057. feature "EPHB4" (0.000040)
2058. feature "SLC25A25-AS1" (0.000040)
2059. feature "SBNO2" (0.000040)
2060. feature "GMPR2" (0.000040)
2061. feature "ARHGEF18" (0.000040)
2062. feature "ARHGAP42" (0.000040)
2063. feature "RETREG2" (0.000040)
2064. feature "SRD5A1" (0.000040)
2065. feature "MASTL" (0.000040)
2066. feature "AVEN" (0.000040)
2067. feature "MT-TL1" (0.000040)
2068. feature "CTDSP2" (0.000040)
2069. feature "SLC25A46" (0.000040)
2070. feature "TTLL12" (0.000040)
2071. feature "CRK" (0.000040)
2072. feature "APPL1" (0.000040)
2073. feature "KRT16" (0.000040)
2074. feature "CBWD3" (0.000040)
2075. feature "PRR12" (0.000040)
2076. feature "VAMP7" (0.000040)
2077. feature "DCTN6-DT" (0.000040)
2078. feature "FBXW4" (0.000040)
2079. feature "PACS2" (0.000040)
2080. feature "VPS9D1-AS1" (0.000040)
2081. feature "ZNF8" (0.000040)
2082. feature "RIC8A" (0.000040)
2083. feature "TMEM39B" (0.000040)
2084. feature "CEP104" (0.000039)
2085. feature "NXPE3" (0.000039)
2086. feature "CHERP" (0.000039)
2087. feature "ZNF282" (0.000039)
2088. feature "AGBL5" (0.000039)
2089. feature "AKT1" (0.000039)
2090. feature "ODF2L" (0.000039)
2091. feature "SBF2" (0.000039)
2092. feature "CXXC5" (0.000039)
2093. feature "PRKCD" (0.000039)
2094. feature "SMIM26" (0.000039)
2095. feature "INF2" (0.000039)
2096. feature "KDM6B" (0.000039)
2097. feature "ZNF48" (0.000039)
2098. feature "BLOC1S3" (0.000039)
2099. feature "KIAA0753" (0.000039)
2100. feature "JPH1" (0.000039)
2101. feature "NADK" (0.000039)
2102. feature "KBTBD7" (0.000039)
2103. feature "ARHGEF11" (0.000039)
2104. feature "CLMN" (0.000039)
2105. feature "MAGEA3" (0.000038)
2106. feature "SMIM4" (0.000038)
2107. feature "PLP2" (0.000038)
2108. feature "GPR153" (0.000038)
2109. feature "CDK14" (0.000038)
2110. feature "CDC25B" (0.000038)
2111. feature "VPS52" (0.000038)
2112. feature "ZBTB26" (0.000038)
2113. feature "GLCCI1" (0.000038)
2114. feature "TRMT61A" (0.000038)
2115. feature "NMI" (0.000038)
2116. feature "THNSL1" (0.000038)
2117. feature "ARSA" (0.000038)
2118. feature "TPP1" (0.000038)
2119. feature "IDH3G" (0.000038)
2120. feature "SNHG18" (0.000038)
2121. feature "FOXK1" (0.000038)
2122. feature "IQANK1" (0.000038)
2123. feature "GTF3C5" (0.000038)
2124. feature "SMAD3" (0.000038)
2125. feature "HMBS" (0.000038)
2126. feature "MAGEA4" (0.000038)
2127. feature "BIN1" (0.000038)
2128. feature "ELF4" (0.000038)
2129. feature "HEATR3" (0.000038)
2130. feature "NKIRAS2" (0.000038)
2131. feature "UAP1L1" (0.000038)
2132. feature "EIF3F" (0.000038)
2133. feature "ANKRD13D" (0.000038)
2134. feature "LRRC73" (0.000038)
2135. feature "ZWILCH" (0.000037)
2136. feature "CNR2" (0.000037)
2137. feature "GLIS2" (0.000037)
2138. feature "MT1F" (0.000037)
2139. feature "CLUH" (0.000037)
2140. feature "DDX19A" (0.000037)
2141. feature "MAD2L1BP" (0.000037)
2142. feature "FTH1" (0.000037)
2143. feature "RHPN1" (0.000037)
2144. feature "AP1G2" (0.000037)
2145. feature "SNHG30" (0.000037)
2146. feature "SESN2" (0.000037)
2147. feature "SLC25A44" (0.000037)
2148. feature "PREB" (0.000037)
2149. feature "GAREM1" (0.000037)
2150. feature "ATP5MC2" (0.000037)
2151. feature "PTPRE" (0.000037)
2152. feature "ZDHHC5" (0.000037)
2153. feature "HEY1" (0.000037)
2154. feature "TXNRD2" (0.000037)
2155. feature "WDR62" (0.000037)
2156. feature "AIP" (0.000037)
2157. feature "PLEKHH3" (0.000037)
2158. feature "USP45" (0.000037)
2159. feature "METTL21A" (0.000037)
2160. feature "ZNF444" (0.000037)
2161. feature "INTS3" (0.000037)
2162. feature "PITPNM1" (0.000037)
2163. feature "FTO" (0.000037)
2164. feature "PSMD2" (0.000037)
2165. feature "GATD1" (0.000037)
2166. feature "HAUS7" (0.000037)
2167. feature "IRF2BP2" (0.000037)
2168. feature "MACROH2A2" (0.000037)
2169. feature "LPAR4" (0.000036)
2170. feature "FOXK2" (0.000036)
2171. feature "PIM3" (0.000036)
2172. feature "TTC39A" (0.000036)
2173. feature "DAPK3" (0.000036)
2174. feature "RCC1" (0.000036)
2175. feature "ASF1B" (0.000036)
2176. feature "DPP9" (0.000036)
2177. feature "CCDC149" (0.000036)
2178. feature "GAMT" (0.000036)
2179. feature "RAPGEFL1" (0.000036)
2180. feature "P4HA2" (0.000036)
2181. feature "VSIR" (0.000036)
2182. feature "FBXL18" (0.000036)
2183. feature "CC2D1A" (0.000036)
2184. feature "PABIR2" (0.000036)
2185. feature "ADGRV1" (0.000036)
2186. feature "TUBA5P" (0.000036)
2187. feature "PIK3CA" (0.000036)
2188. feature "MTARC1" (0.000036)
2189. feature "THAP1" (0.000036)
2190. feature "XRRA1" (0.000036)
2191. feature "RUNDC1" (0.000036)
2192. feature "TNFRSF10B" (0.000036)
2193. feature "TSHZ1" (0.000036)
2194. feature "MID1IP1" (0.000036)
2195. feature "ACYP2" (0.000036)
2196. feature "CTNNAL1" (0.000036)
2197. feature "MANBAL" (0.000036)
2198. feature "CST6" (0.000036)
2199. feature "PPP1R8" (0.000036)
2200. feature "AAMDC" (0.000036)
2201. feature "NCK2" (0.000036)
2202. feature "RNASET2" (0.000036)
2203. feature "ST14" (0.000036)
2204. feature "ZNF620" (0.000036)
2205. feature "DHRS4" (0.000036)
2206. feature "CEP162" (0.000035)
2207. feature "ELP1" (0.000035)
2208. feature "CPEB4" (0.000035)
2209. feature "KIF26A" (0.000035)
2210. feature "AK1" (0.000035)
2211. feature "PGAM5" (0.000035)
2212. feature "FPGS" (0.000035)
2213. feature "RTL8A" (0.000035)
2214. feature "IL18" (0.000035)
2215. feature "CCNG2" (0.000035)
2216. feature "GTF3C2" (0.000035)
2217. feature "SUCLG2" (0.000035)
2218. feature "PDPK1" (0.000035)
2219. feature "GRIN2D" (0.000035)
2220. feature "PLAA" (0.000035)
2221. feature "TIGAR" (0.000035)
2222. feature "MAN2B2" (0.000035)
2223. feature "RUNX2" (0.000035)
2224. feature "HCG11" (0.000035)
2225. feature "INSR" (0.000035)
2226. feature "CABLES2" (0.000035)
2227. feature "MED11" (0.000035)
2228. feature "KIF18B" (0.000035)
2229. feature "UNC119" (0.000035)
2230. feature "AMZ2P1" (0.000034)
2231. feature "NRSN2-AS1" (0.000034)
2232. feature "STYX" (0.000034)
2233. feature "ZMIZ2" (0.000034)
2234. feature "MCM3AP" (0.000034)
2235. feature "ABCA1" (0.000034)
2236. feature "MAP1A" (0.000034)
2237. feature "PRSS8" (0.000034)
2238. feature "GNG4" (0.000034)
2239. feature "TGM2" (0.000034)
2240. feature "RAD23A" (0.000034)
2241. feature "FYN" (0.000034)
2242. feature "SLC25A39" (0.000034)
2243. feature "SELENON" (0.000034)
2244. feature "SRGAP2" (0.000034)
2245. feature "ZSCAN18" (0.000034)
2246. feature "C12orf60" (0.000034)
2247. feature "ZZEF1" (0.000034)
2248. feature "GUCD1" (0.000034)
2249. feature "CACNA1H" (0.000034)
2250. feature "IRX4" (0.000034)
2251. feature "PGAP6" (0.000034)
2252. feature "C1QTNF6" (0.000034)
2253. feature "ANKRD28" (0.000034)
2254. feature "PHLDA1" (0.000034)
2255. feature "OR2C3" (0.000034)
2256. feature "NRAV" (0.000034)
2257. feature "MMP2" (0.000034)
2258. feature "UBXN6" (0.000034)
2259. feature "KLHL22" (0.000034)
2260. feature "AGPAT2" (0.000034)
2261. feature "SMTN" (0.000034)
2262. feature "TFIP11" (0.000033)
2263. feature "STK25" (0.000033)
2264. feature "NAXD" (0.000033)
2265. feature "TRAF3IP2" (0.000033)
2266. feature "RBSN" (0.000033)
2267. feature "ACSL4" (0.000033)
2268. feature "SEMA3D" (0.000033)
2269. feature "ACVR1" (0.000033)
2270. feature "BCAM" (0.000033)
2271. feature "RAB15" (0.000033)
2272. feature "PLD5" (0.000033)
2273. feature "DONSON" (0.000033)
2274. feature "PM20D2" (0.000033)
2275. feature "PTPN21" (0.000033)
2276. feature "PRXL2C" (0.000033)
2277. feature "PABPC1L" (0.000033)
2278. feature "TRIOBP" (0.000033)
2279. feature "PRICKLE2" (0.000033)
2280. feature "MTMR4" (0.000033)
2281. feature "CAPG" (0.000033)
2282. feature "PPIL1" (0.000033)
2283. feature "TNPO2" (0.000033)
2284. feature "RPS6KA4" (0.000033)
2285. feature "DISP1" (0.000033)
2286. feature "GNB2" (0.000033)
2287. feature "ZNF768" (0.000033)
2288. feature "GPM6B" (0.000033)
2289. feature "LIPE-AS1" (0.000033)
2290. feature "PRXL2B" (0.000033)
2291. feature "TAF1C" (0.000033)
2292. feature "PAM" (0.000033)
2293. feature "DYNLT1" (0.000033)
2294. feature "ARFGAP1" (0.000033)
2295. feature "DTD1" (0.000033)
2296. feature "SFTA1P" (0.000033)
2297. feature "PDLIM4" (0.000033)
2298. feature "CACTIN" (0.000033)
2299. feature "TRMT1L" (0.000033)
2300. feature "OR4F17" (0.000033)
2301. feature "PANX2" (0.000033)
2302. feature "C19orf54" (0.000033)
2303. feature "NUMBL" (0.000033)
2304. feature "RIPOR1" (0.000032)
2305. feature "OGFRL1" (0.000032)
2306. feature "RENO1" (0.000032)
2307. feature "CLEC11A" (0.000032)
2308. feature "PISD" (0.000032)
2309. feature "ACER3" (0.000032)
2310. feature "SCLY" (0.000032)
2311. feature "MAP3K3" (0.000032)
2312. feature "RAB3B" (0.000032)
2313. feature "POLR3E" (0.000032)
2314. feature "MSTO1" (0.000032)
2315. feature "MICAL2" (0.000032)
2316. feature "CPSF4" (0.000032)
2317. feature "PRDX1" (0.000032)
2318. feature "RAD54L" (0.000032)
2319. feature "DIABLO" (0.000032)
2320. feature "DNAJA4" (0.000032)
2321. feature "MZT1" (0.000032)
2322. feature "C15orf39" (0.000032)
2323. feature "DYNLT3" (0.000032)
2324. feature "JADE3" (0.000032)
2325. feature "ZNF317" (0.000032)
2326. feature "KIAA0895L" (0.000032)
2327. feature "ALDH2" (0.000032)
2328. feature "TRAK2" (0.000032)
2329. feature "PPP1R12B" (0.000032)
2330. feature "PSMF1" (0.000032)
2331. feature "CAMK2B" (0.000032)
2332. feature "SAMD8" (0.000032)
2333. feature "FBXO46" (0.000032)
2334. feature "MRTFA" (0.000032)
2335. feature "UBN2" (0.000032)
2336. feature "GPR3" (0.000032)
2337. feature "FBXO32" (0.000032)
2338. feature "CASKIN2" (0.000032)
2339. feature "TXN" (0.000032)
2340. feature "AAGAB" (0.000031)
2341. feature "JMJD8" (0.000031)
2342. feature "ESRRA" (0.000031)
2343. feature "MAD2L1" (0.000031)
2344. feature "ZNF764" (0.000031)
2345. feature "TNK2" (0.000031)
2346. feature "TRAF3" (0.000031)
2347. feature "EPN3" (0.000031)
2348. feature "FAM13A" (0.000031)
2349. feature "DBF4B" (0.000031)
2350. feature "DUSP10" (0.000031)
2351. feature "SRSF8" (0.000031)
2352. feature "MMP25-AS1" (0.000031)
2353. feature "COA5" (0.000031)
2354. feature "TRMT5" (0.000031)
2355. feature "SH2B1" (0.000031)
2356. feature "DCAF4" (0.000031)
2357. feature "ZNF335" (0.000031)
2358. feature "E4F1" (0.000031)
2359. feature "UNC13D" (0.000031)
2360. feature "RHOBTB2" (0.000031)
2361. feature "PLPPR2" (0.000031)
2362. feature "PPP2R3B" (0.000031)
2363. feature "ATP6V1H" (0.000031)
2364. feature "MAPKAPK5" (0.000031)
2365. feature "PLCD1" (0.000031)
2366. feature "ELP2" (0.000031)
2367. feature "CUEDC2" (0.000031)
2368. feature "AP1S2" (0.000031)
2369. feature "C16orf74" (0.000031)
2370. feature "DEAF1" (0.000031)
2371. feature "RNFT2" (0.000031)
2372. feature "TUFT1" (0.000031)
2373. feature "MAPK8IP2" (0.000031)
2374. feature "FAM20C" (0.000031)
2375. feature "ENTR1" (0.000031)
2376. feature "MAGED2" (0.000031)
2377. feature "NEBL" (0.000031)
2378. feature "MOCOS" (0.000031)
2379. feature "NETO2" (0.000031)
2380. feature "CIAO3" (0.000031)
2381. feature "TIGD2" (0.000030)
2382. feature "UBE2S" (0.000030)
2383. feature "THBS3" (0.000030)
2384. feature "BICDL1" (0.000030)
2385. feature "HECTD2" (0.000030)
2386. feature "TUBA1B" (0.000030)
2387. feature "OSTF1" (0.000030)
2388. feature "RALGPS1" (0.000030)
2389. feature "MPDZ" (0.000030)
2390. feature "SULT1B1" (0.000030)
2391. feature "PERP" (0.000030)
2392. feature "LINC01151" (0.000030)
2393. feature "MAML2" (0.000030)
2394. feature "MTATP6P1" (0.000030)
2395. feature "WARS2" (0.000030)
2396. feature "RAB8B" (0.000030)
2397. feature "NRP1" (0.000030)
2398. feature "MTFP1" (0.000030)
2399. feature "CRYL1" (0.000030)
2400. feature "FMN1" (0.000030)
2401. feature "TBC1D22A" (0.000030)
2402. feature "TAF8" (0.000030)
2403. feature "TP53I11" (0.000030)
2404. feature "HAND2-AS1" (0.000030)
2405. feature "NAPRT" (0.000030)
2406. feature "HAS2" (0.000030)
2407. feature "NTAN1" (0.000030)
2408. feature "RPGR" (0.000030)
2409. feature "FUT2" (0.000030)
2410. feature "EEF2K" (0.000030)
2411. feature "ABCB9" (0.000030)
2412. feature "SPDYE3" (0.000030)
2413. feature "ZNF33B" (0.000030)
2414. feature "KCTD1" (0.000030)
2415. feature "CDK7" (0.000030)
2416. feature "LRATD1" (0.000030)
2417. feature "FBXL20" (0.000030)
2418. feature "NSMAF" (0.000030)
2419. feature "PLK4" (0.000030)
2420. feature "DNA2" (0.000030)
2421. feature "TFPI2" (0.000030)
2422. feature "MT-TM" (0.000030)
2423. feature "PHF12" (0.000030)
2424. feature "POLA2" (0.000030)
2425. feature "PPRC1" (0.000030)
2426. feature "SETD1A" (0.000030)
2427. feature "ATF3" (0.000030)
2428. feature "ELOVL7" (0.000030)
2429. feature "ITGB8-AS1" (0.000029)
2430. feature "BEND7" (0.000029)
2431. feature "TMEM94" (0.000029)
2432. feature "SLC52A1" (0.000029)
2433. feature "ZNF696" (0.000029)
2434. feature "ALG13" (0.000029)
2435. feature "ARHGAP39" (0.000029)
2436. feature "MAFK" (0.000029)
2437. feature "CDADC1" (0.000029)
2438. feature "GCAT" (0.000029)
2439. feature "FMNL3" (0.000029)
2440. feature "GATA2-AS1" (0.000029)
2441. feature "ENTPD1" (0.000029)
2442. feature "FBXO2" (0.000029)
2443. feature "TFE3" (0.000029)
2444. feature "GPT2" (0.000029)
2445. feature "BATF" (0.000029)
2446. feature "FBXO48" (0.000029)
2447. feature "SQOR" (0.000029)
2448. feature "SOX13" (0.000029)
2449. feature "HNF4A" (0.000029)
2450. feature "DENND1B" (0.000029)
2451. feature "TCEANC" (0.000029)
2452. feature "FKTN" (0.000029)
2453. feature "SERGEF" (0.000029)
2454. feature "LHPP" (0.000029)
2455. feature "AUNIP" (0.000029)
2456. feature "TBC1D4" (0.000029)
2457. feature "ENKD1" (0.000029)
2458. feature "MOV10" (0.000029)
2459. feature "IMPA2" (0.000029)
2460. feature "RCC1L" (0.000029)
2461. feature "DGUOK-AS1" (0.000029)
2462. feature "ANKZF1" (0.000029)
2463. feature "ZFP36" (0.000029)
2464. feature "KLF10" (0.000029)
2465. feature "IGHMBP2" (0.000029)
2466. feature "APBA2" (0.000029)
2467. feature "ERVK3-1" (0.000029)
2468. feature "MAP3K10" (0.000029)
2469. feature "WDFY2" (0.000029)
2470. feature "FASTKD5" (0.000028)
2471. feature "HIP1R" (0.000028)
2472. feature "CEP85" (0.000028)
2473. feature "CEP170B" (0.000028)
2474. feature "BEX5" (0.000028)
2475. feature "C14orf28" (0.000028)
2476. feature "ELMOD3" (0.000028)
2477. feature "CEBPB" (0.000028)
2478. feature "PLA2G15" (0.000028)
2479. feature "BACH2" (0.000028)
2480. feature "RNASEH1-AS1" (0.000028)
2481. feature "TRAPPC14" (0.000028)
2482. feature "SDF2L1" (0.000028)
2483. feature "ARVCF" (0.000028)
2484. feature "ATXN7L1" (0.000028)
2485. feature "RAB40C" (0.000028)
2486. feature "SNX8" (0.000028)
2487. feature "GSDME" (0.000028)
2488. feature "HEBP1" (0.000028)
2489. feature "RAP1GAP2" (0.000028)
2490. feature "KAT2B" (0.000028)
2491. feature "HELZ2" (0.000028)
2492. feature "TPRN" (0.000028)
2493. feature "ZBTB33" (0.000028)
2494. feature "TMX3" (0.000028)
2495. feature "C11orf49" (0.000028)
2496. feature "FAH" (0.000028)
2497. feature "ZNF773" (0.000028)
2498. feature "CAPN15" (0.000028)
2499. feature "ZNF395" (0.000028)
2500. feature "TGFB1" (0.000028)
2501. feature "UPP1" (0.000028)
2502. feature "IKBKG" (0.000028)
2503. feature "COA7" (0.000028)
2504. feature "ZNF578" (0.000028)
2505. feature "ACVR2B" (0.000028)
2506. feature "DTX3L" (0.000028)
2507. feature "CIC" (0.000028)
2508. feature "ZDHHC8" (0.000028)
2509. feature "E2F1" (0.000028)
2510. feature "SH3BP5L" (0.000028)
2511. feature "DEPDC1B" (0.000028)
2512. feature "TBC1D7" (0.000028)
2513. feature "VPS18" (0.000028)
2514. feature "AFG3L1P" (0.000028)
2515. feature "LRCH3" (0.000027)
2516. feature "FDXR" (0.000027)
2517. feature "OVCH1-AS1" (0.000027)
2518. feature "BMPR1A" (0.000027)
2519. feature "DCLRE1A" (0.000027)
2520. feature "FBXO7" (0.000027)
2521. feature "CCDC18" (0.000027)
2522. feature "MAN2B1" (0.000027)
2523. feature "ONECUT2" (0.000027)
2524. feature "ZMAT3" (0.000027)
2525. feature "POLB" (0.000027)
2526. feature "RBM24" (0.000027)
2527. feature "PTPN23" (0.000027)
2528. feature "SLC25A22" (0.000027)
2529. feature "IFFO2" (0.000027)
2530. feature "TWIST1" (0.000027)
2531. feature "LIPT1" (0.000027)
2532. feature "FHIP1B" (0.000027)
2533. feature "HELB" (0.000027)
2534. feature "ABCF2" (0.000027)
2535. feature "ANKEF1" (0.000027)
2536. feature "FOXN3-AS1" (0.000027)
2537. feature "FDX2" (0.000027)
2538. feature "PRKAG1" (0.000027)
2539. feature "ZIC2" (0.000027)
2540. feature "EYA3" (0.000027)
2541. feature "NAA16" (0.000027)
2542. feature "BICC1" (0.000027)
2543. feature "NUDT8" (0.000027)
2544. feature "TBP" (0.000027)
2545. feature "FGFR3" (0.000027)
2546. feature "GTF2IRD1" (0.000027)
2547. feature "NAGLU" (0.000027)
2548. feature "NKX3-1" (0.000027)
2549. feature "CADM1" (0.000027)
2550. feature "FAAP100" (0.000027)
2551. feature "KHK" (0.000027)
2552. feature "JAKMIP3" (0.000027)
2553. feature "ESR1" (0.000027)
2554. feature "THAP4" (0.000027)
2555. feature "PYCR3" (0.000027)
2556. feature "GJB3" (0.000027)
2557. feature "LRRC69" (0.000027)
2558. feature "MED18" (0.000027)
2559. feature "NECTIN2" (0.000027)
2560. feature "TRAFD1" (0.000027)
2561. feature "ATXN7L2" (0.000027)
2562. feature "NFATC3" (0.000027)
2563. feature "DOCK9" (0.000027)
2564. feature "ZNF184" (0.000027)
2565. feature "SRC" (0.000027)
2566. feature "ZNF718" (0.000027)
2567. feature "PCED1B-AS1" (0.000027)
2568. feature "POLR3GL" (0.000027)
2569. feature "MTCO1P12" (0.000027)
2570. feature "NAA40" (0.000027)
2571. feature "RAD9A" (0.000027)
2572. feature "ATG4C" (0.000027)
2573. feature "CRAMP1" (0.000027)
2574. feature "TRDN" (0.000026)
2575. feature "TRIM69" (0.000026)
2576. feature "MIOS" (0.000026)
2577. feature "RNF19B" (0.000026)
2578. feature "CRYZL1" (0.000026)
2579. feature "SNX33" (0.000026)
2580. feature "TAF1A" (0.000026)
2581. feature "CALHM2" (0.000026)
2582. feature "IPO13" (0.000026)
2583. feature "IMPACT" (0.000026)
2584. feature "MED23" (0.000026)
2585. feature "ZNF180" (0.000026)
2586. feature "DYRK1B" (0.000026)
2587. feature "PLCL2" (0.000026)
2588. feature "KIF13B" (0.000026)
2589. feature "DCAKD" (0.000026)
2590. feature "YPEL5" (0.000026)
2591. feature "TRIM21" (0.000026)
2592. feature "SLC66A2" (0.000026)
2593. feature "NCOA5" (0.000026)
2594. feature "C2orf27A" (0.000026)
2595. feature "GEN1" (0.000026)
2596. feature "ZUP1" (0.000026)
2597. feature "ANKRD27" (0.000026)
2598. feature "LIN54" (0.000026)
2599. feature "SLC2A6" (0.000026)
2600. feature "EDC4" (0.000026)
2601. feature "HPS1" (0.000026)
2602. feature "PIH1D2" (0.000026)
2603. feature "PEF1" (0.000026)
2604. feature "CEP72" (0.000026)
2605. feature "PRELID2" (0.000026)
2606. feature "PLEKHG4B" (0.000026)
2607. feature "TBC1D16" (0.000026)
2608. feature "IQSEC2" (0.000026)
2609. feature "ZCCHC3" (0.000026)
2610. feature "EPHA2" (0.000026)
2611. feature "WDR90" (0.000026)
2612. feature "TGFB1I1" (0.000026)
2613. feature "PLEKHO1" (0.000026)
2614. feature "CXADR" (0.000026)
2615. feature "PPTC7" (0.000026)
2616. feature "FAM114A2" (0.000026)
2617. feature "MRPL49" (0.000026)
2618. feature "SULT2B1" (0.000026)
2619. feature "MIR200CHG" (0.000026)
2620. feature "LCA5" (0.000026)
2621. feature "TENT2" (0.000026)
2622. feature "CEBPG" (0.000026)
2623. feature "LINC01232" (0.000026)
2624. feature "FAM47E" (0.000026)
2625. feature "NCDN" (0.000026)
2626. feature "LRAT" (0.000026)
2627. feature "PHF21A" (0.000026)
2628. feature "ADAP1" (0.000026)
2629. feature "TBC1D22B" (0.000026)
2630. feature "CCDC97" (0.000026)
2631. feature "CASTOR2" (0.000026)
2632. feature "LINC00491" (0.000026)
2633. feature "GPN1" (0.000025)
2634. feature "ZDHHC4" (0.000025)
2635. feature "ZNF654" (0.000025)
2636. feature "TADA2B" (0.000025)
2637. feature "ZNF865" (0.000025)
2638. feature "SNHG17" (0.000025)
2639. feature "LTBP3" (0.000025)
2640. feature "ZNF329" (0.000025)
2641. feature "IKZF4" (0.000025)
2642. feature "SAP30-DT" (0.000025)
2643. feature "WDFY3-AS2" (0.000025)
2644. feature "UBALD1" (0.000025)
2645. feature "SLC9A8" (0.000025)
2646. feature "PIP4P2" (0.000025)
2647. feature "PTAR1" (0.000025)
2648. feature "ULK2" (0.000025)
2649. feature "ATP1A1-AS1" (0.000025)
2650. feature "MIB2" (0.000025)
2651. feature "MYO9B" (0.000025)
2652. feature "FBXO27" (0.000025)
2653. feature "ADD2" (0.000025)
2654. feature "TJAP1" (0.000025)
2655. feature "KIRREL1" (0.000025)
2656. feature "ARHGEF39" (0.000025)
2657. feature "DLX1" (0.000025)
2658. feature "RAB17" (0.000025)
2659. feature "PIGC" (0.000025)
2660. feature "SEMA4C" (0.000025)
2661. feature "IFRD2" (0.000025)
2662. feature "ARHGAP26" (0.000025)
2663. feature "LINC01750" (0.000025)
2664. feature "NEDD9" (0.000025)
2665. feature "HEIH" (0.000025)
2666. feature "ITGB6" (0.000025)
2667. feature "SRXN1" (0.000025)
2668. feature "ITPKC" (0.000025)
2669. feature "CDKL1" (0.000025)
2670. feature "SLC43A2" (0.000025)
2671. feature "SPINDOC" (0.000025)
2672. feature "ALDH4A1" (0.000025)
2673. feature "NRGN" (0.000025)
2674. feature "RAB11FIP5" (0.000025)
2675. feature "NAIF1" (0.000025)
2676. feature "SLC39A14" (0.000025)
2677. feature "LINC02482" (0.000025)
2678. feature "NDEL1" (0.000025)
2679. feature "TSEN54" (0.000025)
2680. feature "BIN3" (0.000025)
2681. feature "MEX3D" (0.000025)
2682. feature "ASB6" (0.000025)
2683. feature "RRAGC" (0.000025)
2684. feature "UNK" (0.000025)
2685. feature "SAP30L" (0.000025)
2686. feature "DHCR7" (0.000025)
2687. feature "CUTC" (0.000025)
2688. feature "MSX2" (0.000024)
2689. feature "ZNF875" (0.000024)
2690. feature "KHDC1" (0.000024)
2691. feature "NEFL" (0.000024)
2692. feature "KBTBD4" (0.000024)
2693. feature "COL12A1" (0.000024)
2694. feature "IRGQ" (0.000024)
2695. feature "NOSTRIN" (0.000024)
2696. feature "FAM171B" (0.000024)
2697. feature "TMEM126A" (0.000024)
2698. feature "YEATS2" (0.000024)
2699. feature "KCTD18" (0.000024)
2700. feature "C1orf53" (0.000024)
2701. feature "DNAJB2" (0.000024)
2702. feature "CBWD6" (0.000024)
2703. feature "PARP9" (0.000024)
2704. feature "TRIM16L" (0.000024)
2705. feature "KRTAP5-AS1" (0.000024)
2706. feature "TROAP" (0.000024)
2707. feature "LAMA3" (0.000024)
2708. feature "ZNF446" (0.000024)
2709. feature "ZHX2" (0.000024)
2710. feature "DECR2" (0.000024)
2711. feature "ERVK13-1" (0.000024)
2712. feature "KIAA0825" (0.000024)
2713. feature "GRB14" (0.000024)
2714. feature "DLK2" (0.000024)
2715. feature "EEF1A2" (0.000024)
2716. feature "MRPS36" (0.000024)
2717. feature "MEGF6" (0.000024)
2718. feature "ZNF597" (0.000024)
2719. feature "LINC02484" (0.000024)
2720. feature "FNBP1P1" (0.000024)
2721. feature "ATP5PDP3" (0.000024)
2722. feature "MTO1" (0.000024)
2723. feature "IPMK" (0.000024)
2724. feature "CAMKK1" (0.000024)
2725. feature "SNHG19" (0.000023)
2726. feature "BCL2L15" (0.000023)
2727. feature "PRPF40B" (0.000023)
2728. feature "UBALD2" (0.000023)
2729. feature "EN2" (0.000023)
2730. feature "NCAPG" (0.000023)
2731. feature "EIF3J-DT" (0.000023)
2732. feature "ZFP30" (0.000023)
2733. feature "TNFAIP2" (0.000023)
2734. feature "FABP6" (0.000023)
2735. feature "PROM2" (0.000023)
2736. feature "CDK8" (0.000023)
2737. feature "SMARCAL1" (0.000023)
2738. feature "WARS1" (0.000023)
2739. feature "FOSB" (0.000023)
2740. feature "PGBD1" (0.000023)
2741. feature "PACC1" (0.000023)
2742. feature "CXCL8" (0.000023)
2743. feature "DUSP8" (0.000023)
2744. feature "ST8SIA6-AS1" (0.000023)
2745. feature "RCHY1" (0.000023)
2746. feature "TEPSIN" (0.000023)
2747. feature "NOLC1" (0.000023)
2748. feature "RAP2A" (0.000023)
2749. feature "BAHD1" (0.000023)
2750. feature "NXNL2" (0.000023)
2751. feature "PROSER3" (0.000023)
2752. feature "CCM2" (0.000023)
2753. feature "TRIM66" (0.000023)
2754. feature "LTBP4" (0.000023)
2755. feature "HERC6" (0.000023)
2756. feature "HLA-C" (0.000023)
2757. feature "ZNF442" (0.000023)
2758. feature "IL21R" (0.000023)
2759. feature "PTK2B" (0.000023)
2760. feature "NFKBIZ" (0.000023)
2761. feature "CCDC71L" (0.000023)
2762. feature "SLC29A2" (0.000023)
2763. feature "MFAP2" (0.000023)
2764. feature "CALML5" (0.000023)
2765. feature "REPS2" (0.000023)
2766. feature "SLC25A43" (0.000023)
2767. feature "MLH3" (0.000023)
2768. feature "NDST1" (0.000023)
2769. feature "HES6" (0.000023)
2770. feature "ZFYVE27" (0.000023)
2771. feature "GSK3A" (0.000023)
2772. feature "CCDC88B" (0.000023)
2773. feature "PRKCH" (0.000023)
2774. feature "MTLN" (0.000023)
2775. feature "ARHGEF37" (0.000023)
2776. feature "SPATA6L" (0.000023)
2777. feature "PCLO" (0.000023)
2778. feature "RASSF8" (0.000023)
2779. feature "MAP3K1" (0.000023)
2780. feature "DFFB" (0.000023)
2781. feature "PRR36" (0.000023)
2782. feature "MBTPS1" (0.000022)
2783. feature "EPCAM-DT" (0.000022)
2784. feature "RIC1" (0.000022)
2785. feature "PDIA5" (0.000022)
2786. feature "PLCG1" (0.000022)
2787. feature "ARL17B" (0.000022)
2788. feature "BCKDHB" (0.000022)
2789. feature "WDR74" (0.000022)
2790. feature "CLASRP" (0.000022)
2791. feature "SMIM19" (0.000022)
2792. feature "PTPRU" (0.000022)
2793. feature "KIFC2" (0.000022)
2794. feature "ANKMY1" (0.000022)
2795. feature "VPS16" (0.000022)
2796. feature "CARD16" (0.000022)
2797. feature "UNC119B" (0.000022)
2798. feature "HIVEP3" (0.000022)
2799. feature "EGLN2" (0.000022)
2800. feature "ZNF35" (0.000022)
2801. feature "CDHR3" (0.000022)
2802. feature "TPTEP2" (0.000022)
2803. feature "ANKRD54" (0.000022)
2804. feature "DMD" (0.000022)
2805. feature "INSIG1" (0.000022)
2806. feature "ZIC4" (0.000022)
2807. feature "LINC00052" (0.000022)
2808. feature "MIATNB" (0.000022)
2809. feature "ZNF524" (0.000022)
2810. feature "SLC25A53" (0.000022)
2811. feature "PIKFYVE" (0.000022)
2812. feature "TRMT12" (0.000022)
2813. feature "WDR5B-DT" (0.000022)
2814. feature "YJU2" (0.000022)
2815. feature "ZKSCAN5" (0.000022)
2816. feature "TRAPPC9" (0.000022)
2817. feature "INPP5E" (0.000022)
2818. feature "TSPAN15" (0.000022)
2819. feature "RNF157" (0.000022)
2820. feature "ZNF813" (0.000022)
2821. feature "PANX1" (0.000022)
2822. feature "TNFRSF19" (0.000022)
2823. feature "RPL22L1" (0.000022)
2824. feature "SYT12" (0.000022)
2825. feature "PPIF" (0.000022)
2826. feature "WDR24" (0.000022)
2827. feature "OVOL1" (0.000022)
2828. feature "RNF183" (0.000022)
2829. feature "PTDSS2" (0.000022)
2830. feature "SMIM29" (0.000022)
2831. feature "EAF2" (0.000022)
2832. feature "LINC00326" (0.000022)
2833. feature "HOXC13-AS" (0.000022)
2834. feature "NHS" (0.000022)
2835. feature "HAP1" (0.000022)
2836. feature "SLC29A3" (0.000022)
2837. feature "SCNN1A" (0.000022)
2838. feature "LDB1" (0.000022)
2839. feature "PLCB1" (0.000021)
2840. feature "LINC01128" (0.000021)
2841. feature "MMP15" (0.000021)
2842. feature "ZNF254" (0.000021)
2843. feature "CDK5" (0.000021)
2844. feature "USP24" (0.000021)
2845. feature "NEU1" (0.000021)
2846. feature "CCN1" (0.000021)
2847. feature "TRAIP" (0.000021)
2848. feature "ROM1" (0.000021)
2849. feature "TRIM14" (0.000021)
2850. feature "THAP3" (0.000021)
2851. feature "DOLPP1" (0.000021)
2852. feature "TRAM2" (0.000021)
2853. feature "PHEX" (0.000021)
2854. feature "NT5C" (0.000021)
2855. feature "CERCAM" (0.000021)
2856. feature "JPH3" (0.000021)
2857. feature "KIAA0040" (0.000021)
2858. feature "UBTD1" (0.000021)
2859. feature "OXSM" (0.000021)
2860. feature "RNF146" (0.000021)
2861. feature "ITGB5" (0.000021)
2862. feature "PLIN3" (0.000021)
2863. feature "ZCWPW1" (0.000021)
2864. feature "MXD3" (0.000021)
2865. feature "PKP1" (0.000021)
2866. feature "BET1" (0.000021)
2867. feature "PIK3R2" (0.000021)
2868. feature "IGSF6" (0.000021)
2869. feature "POLM" (0.000021)
2870. feature "FARS2" (0.000021)
2871. feature "MFSD5" (0.000021)
2872. feature "ALDH6A1" (0.000021)
2873. feature "ZBTB12" (0.000021)
2874. feature "FAM219B" (0.000021)
2875. feature "MFSD2B" (0.000021)
2876. feature "UBIAD1" (0.000021)
2877. feature "SLC13A5" (0.000021)
2878. feature "HYAL2" (0.000021)
2879. feature "PAQR3" (0.000021)
2880. feature "FSBP" (0.000021)
2881. feature "GSTM3" (0.000021)
2882. feature "MAPK12" (0.000021)
2883. feature "TSC22D2" (0.000021)
2884. feature "LINC-PINT" (0.000021)
2885. feature "ARMH3" (0.000021)
2886. feature "IBA57" (0.000021)
2887. feature "LINC00885" (0.000021)
2888. feature "TSPAN17" (0.000021)
2889. feature "RRAGA" (0.000021)
2890. feature "NTN4" (0.000021)
2891. feature "BET1L" (0.000021)
2892. feature "ELF3" (0.000021)
2893. feature "SAT2" (0.000021)
2894. feature "XRCC1" (0.000021)
2895. feature "MID2" (0.000021)
2896. feature "SH3D19" (0.000021)
2897. feature "UST" (0.000021)
2898. feature "FAM193A" (0.000021)
2899. feature "GPATCH3" (0.000021)
2900. feature "PI4K2B" (0.000021)
2901. feature "RSPH3" (0.000021)
2902. feature "TMPRSS4" (0.000021)
2903. feature "ZNF99" (0.000021)
2904. feature "ZNF862" (0.000021)
2905. feature "ITM2C" (0.000021)
2906. feature "AGAP2-AS1" (0.000021)
2907. feature "IPPK" (0.000021)
2908. feature "RASSF7" (0.000021)
2909. feature "OAZ3" (0.000021)
2910. feature "BOK" (0.000021)
2911. feature "TMEM200A" (0.000021)
2912. feature "EFNA1" (0.000021)
2913. feature "CCDC18-AS1" (0.000021)
2914. feature "SPHK1" (0.000021)
2915. feature "ZFP41" (0.000021)
2916. feature "RAI1" (0.000021)
2917. feature "SLC40A1" (0.000020)
2918. feature "ERG28" (0.000020)
2919. feature "NANP" (0.000020)
2920. feature "RIPOR2" (0.000020)
2921. feature "TPBG" (0.000020)
2922. feature "SLC25A12" (0.000020)
2923. feature "HECA" (0.000020)
2924. feature "SUPT3H" (0.000020)
2925. feature "TGFB2" (0.000020)
2926. feature "SRCAP" (0.000020)
2927. feature "NARS2" (0.000020)
2928. feature "GORASP1" (0.000020)
2929. feature "TKFC" (0.000020)
2930. feature "THNSL2" (0.000020)
2931. feature "TXNRD3" (0.000020)
2932. feature "SLC7A11" (0.000020)
2933. feature "TPCN1" (0.000020)
2934. feature "RBP1" (0.000020)
2935. feature "FAM222A" (0.000020)
2936. feature "LINC00683" (0.000020)
2937. feature "FGFBP3" (0.000020)
2938. feature "ABCD4" (0.000020)
2939. feature "MAPK3" (0.000020)
2940. feature "RASGEF1A" (0.000020)
2941. feature "HES2" (0.000020)
2942. feature "SHROOM2" (0.000020)
2943. feature "NPFFR1" (0.000020)
2944. feature "LPCAT4" (0.000020)
2945. feature "FOXP4-AS1" (0.000020)
2946. feature "CROCCP3" (0.000020)
2947. feature "TTC7A" (0.000020)
2948. feature "ST6GAL1" (0.000020)
2949. feature "C10orf88" (0.000020)
2950. feature "IMPA1" (0.000020)
2951. feature "LINC00475" (0.000020)
2952. feature "GNPDA2" (0.000020)
2953. feature "LYPD6" (0.000020)
2954. feature "KIF20A" (0.000020)
2955. feature "ABCG1" (0.000020)
2956. feature "ZNF804A" (0.000020)
2957. feature "MORN1" (0.000020)
2958. feature "PC" (0.000020)
2959. feature "MUC5AC" (0.000020)
2960. feature "PSCA" (0.000020)
2961. feature "ZNF571-AS1" (0.000020)
2962. feature "KIFC3" (0.000020)
2963. feature "RALGDS" (0.000020)
2964. feature "MID1" (0.000020)
2965. feature "DYSF" (0.000020)
2966. feature "S100A4" (0.000020)
2967. feature "IL6R" (0.000020)
2968. feature "OCRL" (0.000020)
2969. feature "LINC01116" (0.000020)
2970. feature "OR9H1P" (0.000020)
2971. feature "TEX9" (0.000020)
2972. feature "ADCK1" (0.000020)
2973. feature "ECHDC2" (0.000020)
2974. feature "FOXM1" (0.000020)
2975. feature "SS18" (0.000020)
2976. feature "DDX28" (0.000020)
2977. feature "RTN4RL1" (0.000019)
2978. feature "CASP3" (0.000019)
2979. feature "DLX6" (0.000019)
2980. feature "OLFM2" (0.000019)
2981. feature "SLC35A2" (0.000019)
2982. feature "VMAC" (0.000019)
2983. feature "NLGN2" (0.000019)
2984. feature "ZDHHC18" (0.000019)
2985. feature "FAM53C" (0.000019)
2986. feature "SPIRE2" (0.000019)
2987. feature "NOVA1" (0.000019)
2988. feature "LRRC20" (0.000019)
2989. feature "ULK3" (0.000019)
2990. feature "CHD2" (0.000019)
2991. feature "MCEE" (0.000019)
2992. feature "HNRNPCP3" (0.000019)
2993. feature "ANKH" (0.000019)
2994. feature "RPIA" (0.000019)
2995. feature "MSX1" (0.000019)
2996. feature "ZNF134" (0.000019)
2997. feature "UQCRHL" (0.000019)
2998. feature "LY6D" (0.000019)
2999. feature "S1PR1" (0.000019)
3000. feature "ARHGEF17" (0.000019)
3001. feature "TRPM2" (0.000019)
3002. feature "ANXA6" (0.000019)
3003. feature "CEACAM19" (0.000019)
3004. feature "C5AR1" (0.000019)
3005. feature "SGSM2" (0.000019)
3006. feature "KIF3C" (0.000019)
3007. feature "HAAO" (0.000019)
3008. feature "PALM3" (0.000019)
3009. feature "NES" (0.000019)
3010. feature "INTS14" (0.000019)
3011. feature "TCF7" (0.000019)
3012. feature "UNC50" (0.000019)
3013. feature "CYHR1" (0.000019)
3014. feature "ARMC2" (0.000019)
3015. feature "C6orf136" (0.000019)
3016. feature "NINL" (0.000019)
3017. feature "POLR2J3" (0.000019)
3018. feature "FRMD8" (0.000019)
3019. feature "ATXN7L3" (0.000019)
3020. feature "ALS2CL" (0.000019)
3021. feature "B4GALT2" (0.000019)
3022. feature "AHR" (0.000019)
3023. feature "DNAH7" (0.000019)
3024. feature "MOGAT3" (0.000019)
3025. feature "MOGS" (0.000019)
3026. feature "TUBE1" (0.000019)
3027. feature "FANCF" (0.000019)
3028. feature "CIART" (0.000019)
3029. feature "PSG2" (0.000019)
3030. feature "HIGD1A" (0.000019)
3031. feature "SKIDA1" (0.000019)
3032. feature "CCDC7" (0.000019)
3033. feature "DOP1A" (0.000019)
3034. feature "CXCL1" (0.000019)
3035. feature "CYP1B1" (0.000019)
3036. feature "CAMTA2" (0.000019)
3037. feature "ILDR1" (0.000019)
3038. feature "TXNRD1" (0.000019)
3039. feature "FAM241B" (0.000019)
3040. feature "AP1B1" (0.000019)
3041. feature "DDAH1" (0.000019)
3042. feature "ARNTL" (0.000019)
3043. feature "ARL6IP1" (0.000019)
3044. feature "TMEM125" (0.000019)
3045. feature "FRAT1" (0.000019)
3046. feature "C1orf112" (0.000019)
3047. feature "MIS12" (0.000019)
3048. feature "TLE2" (0.000019)
3049. feature "ST3GAL1-DT" (0.000019)
3050. feature "RBMS3-AS3" (0.000019)
3051. feature "NSMCE3" (0.000019)
3052. feature "PXDN" (0.000018)
3053. feature "ATG101" (0.000018)
3054. feature "BOLA3-AS1" (0.000018)
3055. feature "OBSCN" (0.000018)
3056. feature "PHLPP1" (0.000018)
3057. feature "BDH1" (0.000018)
3058. feature "SNHG11" (0.000018)
3059. feature "FKBP9" (0.000018)
3060. feature "TSHZ2" (0.000018)
3061. feature "TIMM22" (0.000018)
3062. feature "ARMCX4" (0.000018)
3063. feature "TEAD3" (0.000018)
3064. feature "CRYZ" (0.000018)
3065. feature "DSC2" (0.000018)
3066. feature "HAS3" (0.000018)
3067. feature "ELFN2" (0.000018)
3068. feature "CDR2" (0.000018)
3069. feature "HR" (0.000018)
3070. feature "AMH" (0.000018)
3071. feature "PLA2G4A" (0.000018)
3072. feature "SLC2A8" (0.000018)
3073. feature "PXYLP1" (0.000018)
3074. feature "ACAA2" (0.000018)
3075. feature "CYB561D2" (0.000018)
3076. feature "OR7E13P" (0.000018)
3077. feature "IFI27" (0.000018)
3078. feature "SH3BGRL2" (0.000018)
3079. feature "LINC00173" (0.000018)
3080. feature "DVL1" (0.000018)
3081. feature "TRIM52-AS1" (0.000018)
3082. feature "UBE2QL1" (0.000018)
3083. feature "ZNF581" (0.000018)
3084. feature "RABL2A" (0.000018)
3085. feature "BICRA" (0.000018)
3086. feature "LINC02241" (0.000018)
3087. feature "ZNF731P" (0.000018)
3088. feature "UBE2D4" (0.000018)
3089. feature "OR3A3" (0.000018)
3090. feature "DOC2A" (0.000018)
3091. feature "OR5M2P" (0.000018)
3092. feature "RPUSD4" (0.000018)
3093. feature "TMEM44-AS1" (0.000018)
3094. feature "RAB20" (0.000018)
3095. feature "PGBD2" (0.000018)
3096. feature "PPP1R9B" (0.000018)
3097. feature "FAM169A" (0.000018)
3098. feature "PEX11B" (0.000018)
3099. feature "ZNF469" (0.000018)
3100. feature "DPF1" (0.000018)
3101. feature "PCAT14" (0.000018)
3102. feature "UMAD1" (0.000018)
3103. feature "HMGB1P39" (0.000018)
3104. feature "ARRDC3" (0.000018)
3105. feature "MAPK8IP3" (0.000018)
3106. feature "NBEAL2" (0.000018)
3107. feature "EREG" (0.000018)
3108. feature "TRERF1" (0.000018)
3109. feature "THRA" (0.000018)
3110. feature "PBXIP1" (0.000018)
3111. feature "SVIL-AS1" (0.000018)
3112. feature "PCAT1" (0.000018)
3113. feature "MHENCR" (0.000018)
3114. feature "TEDC2" (0.000018)
3115. feature "CLCF1" (0.000018)
3116. feature "GASAL1" (0.000018)
3117. feature "LINC02913" (0.000018)
3118. feature "GLI4" (0.000018)
3119. feature "MAU2" (0.000018)
3120. feature "TMEM241" (0.000018)
3121. feature "LRFN4" (0.000018)
3122. feature "NOTCH2NLC" (0.000018)
3123. feature "MVK" (0.000018)
3124. feature "LINC02609" (0.000018)
3125. feature "GAS7" (0.000017)
3126. feature "SUN2" (0.000017)
3127. feature "HBEGF" (0.000017)
3128. feature "GMEB1" (0.000017)
3129. feature "SCMH1" (0.000017)
3130. feature "C3orf14" (0.000017)
3131. feature "ZNF433" (0.000017)
3132. feature "APOBEC3F" (0.000017)
3133. feature "C9orf64" (0.000017)
3134. feature "FBXL16" (0.000017)
3135. feature "RNF25" (0.000017)
3136. feature "SAP30L-AS1" (0.000017)
3137. feature "MAPT-AS1" (0.000017)
3138. feature "NFKBIL1" (0.000017)
3139. feature "MFSD13A" (0.000017)
3140. feature "CES2" (0.000017)
3141. feature "LINC00639" (0.000017)
3142. feature "TDRD3" (0.000017)
3143. feature "BRWD1-AS2" (0.000017)
3144. feature "COL27A1" (0.000017)
3145. feature "GKAP1" (0.000017)
3146. feature "ALPK1" (0.000017)
3147. feature "KDM7A-DT" (0.000017)
3148. feature "NUSAP1" (0.000017)
3149. feature "STK35" (0.000017)
3150. feature "IFNGR2" (0.000017)
3151. feature "MICALL1" (0.000017)
3152. feature "TMEM51" (0.000017)
3153. feature "SLC36A4" (0.000017)
3154. feature "RPA1" (0.000017)
3155. feature "FAM107A" (0.000017)
3156. feature "HLCS" (0.000017)
3157. feature "ZBTB42" (0.000017)
3158. feature "TRAPPC2B" (0.000017)
3159. feature "DUOX1" (0.000017)
3160. feature "LSS" (0.000017)
3161. feature "PDE6D" (0.000017)
3162. feature "LACTB2-AS1" (0.000017)
3163. feature "SYNM" (0.000017)
3164. feature "SYVN1" (0.000017)
3165. feature "CYP4B1" (0.000017)
3166. feature "CTNNA1-AS1" (0.000017)
3167. feature "SMPD2" (0.000017)
3168. feature "MYOSLID" (0.000017)
3169. feature "NOC2LP1" (0.000017)
3170. feature "CCNYL1" (0.000017)
3171. feature "SPN" (0.000017)
3172. feature "BICRAL" (0.000017)
3173. feature "PTRHD1" (0.000017)
3174. feature "OR10H1" (0.000017)
3175. feature "SERTAD2" (0.000017)
3176. feature "PPP1R12A-AS1" (0.000017)
3177. feature "TBKBP1" (0.000017)
3178. feature "EPB41L4B" (0.000017)
3179. feature "ATP6V0D1-DT" (0.000017)
3180. feature "H2AZ1" (0.000017)
3181. feature "IGF2BP2" (0.000017)
3182. feature "PYGO1" (0.000017)
3183. feature "TANGO2" (0.000017)
3184. feature "CDC14A" (0.000017)
3185. feature "PARP12" (0.000017)
3186. feature "TBL3" (0.000017)
3187. feature "GOLGA2P7" (0.000017)
3188. feature "SNAPC3" (0.000017)
3189. feature "IFIT1" (0.000017)
3190. feature "AQP11" (0.000017)
3191. feature "SWSAP1" (0.000017)
3192. feature "PLXNA1" (0.000017)
3193. feature "CCN2" (0.000017)
3194. feature "NOA1" (0.000017)
3195. feature "TNFAIP3" (0.000017)
3196. feature "PALM" (0.000017)
3197. feature "PODNL1" (0.000017)
3198. feature "MTF1" (0.000017)
3199. feature "SRP14-DT" (0.000017)
3200. feature "RGP1" (0.000017)
3201. feature "ZNF321P" (0.000017)
3202. feature "VDR" (0.000017)
3203. feature "TRAF4" (0.000017)
3204. feature "TSGA10" (0.000017)
3205. feature "FAM222B" (0.000017)
3206. feature "IL4R" (0.000017)
3207. feature "CCNE2" (0.000017)
3208. feature "AMPD2" (0.000017)
3209. feature "RNF44" (0.000017)
3210. feature "PIP5K1C" (0.000016)
3211. feature "NOP2" (0.000016)
3212. feature "PCSK9" (0.000016)
3213. feature "DCLK1" (0.000016)
3214. feature "TRIM39" (0.000016)
3215. feature "PER1" (0.000016)
3216. feature "NDST2" (0.000016)
3217. feature "C21orf62-AS1" (0.000016)
3218. feature "PLEKHS1" (0.000016)
3219. feature "PRR5L" (0.000016)
3220. feature "PWWP2B" (0.000016)
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3224. feature "SELENOP" (0.000016)
3225. feature "UVSSA" (0.000016)
3226. feature "PKN3" (0.000016)
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3229. feature "C2CD5" (0.000016)
3230. feature "PSMD9" (0.000016)
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3232. feature "DHRS11" (0.000016)
3233. feature "KIF18A" (0.000016)
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3236. feature "CDH6" (0.000016)
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3239. feature "KCP" (0.000016)
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3247. feature "LZTR1" (0.000016)
3248. feature "SH3PXD2A" (0.000016)
3249. feature "KDM5C" (0.000016)
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3253. feature "BCL2L2" (0.000016)
3254. feature "STRIP1" (0.000016)
3255. feature "SPHK2" (0.000016)
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3262. feature "SPSB1" (0.000016)
3263. feature "KBTBD3" (0.000016)
3264. feature "PLEKHG1" (0.000016)
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3268. feature "ZBTB39" (0.000016)
3269. feature "DSG2" (0.000016)
3270. feature "HMGA2" (0.000016)
3271. feature "BCL7B" (0.000016)
3272. feature "LCA5L" (0.000016)
3273. feature "GNAZ" (0.000016)
3274. feature "UBQLN2" (0.000016)
3275. feature "PRR15" (0.000016)
3276. feature "KDF1" (0.000016)
3277. feature "VAMP2" (0.000016)
3278. feature "TTC3P1" (0.000016)
3279. feature "MAZ" (0.000016)
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3282. feature "BMP8A" (0.000016)
3283. feature "MMP16" (0.000016)
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3285. feature "STAT5B" (0.000016)
3286. feature "PEX7" (0.000016)
3287. feature "TMEM25" (0.000016)
3288. feature "FBXL3" (0.000016)
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3291. feature "PARP6" (0.000016)
3292. feature "NOMO3" (0.000016)
3293. feature "ZXDC" (0.000016)
3294. feature "CREB3L1" (0.000016)
3295. feature "NECAP1" (0.000016)
3296. feature "CROCC" (0.000016)
3297. feature "CARD9" (0.000016)
3298. feature "LINC00471" (0.000016)
3299. feature "RAB36" (0.000016)
3300. feature "ZNF473" (0.000016)
3301. feature "TSACC" (0.000016)
3302. feature "ZNF767P" (0.000016)
3303. feature "TESMIN" (0.000016)
3304. feature "RBM15B" (0.000016)
3305. feature "CMTM4" (0.000016)
3306. feature "RADIL" (0.000016)
3307. feature "NUDT17" (0.000016)
3308. feature "ATP6V0C" (0.000016)
3309. feature "ZWINT" (0.000016)
3310. feature "RASGRP1" (0.000016)
3311. feature "CASP1" (0.000016)
3312. feature "MAPK7" (0.000016)
3313. feature "PACSIN1" (0.000015)
3314. feature "FOXJ3" (0.000015)
3315. feature "LIN52" (0.000015)
3316. feature "RILP" (0.000015)
3317. feature "SCOC-AS1" (0.000015)
3318. feature "ZNF699" (0.000015)
3319. feature "SIX2" (0.000015)
3320. feature "AKR1E2" (0.000015)
3321. feature "PER3" (0.000015)
3322. feature "RTL8C" (0.000015)
3323. feature "LINC01206" (0.000015)
3324. feature "SHISA2" (0.000015)
3325. feature "TEF" (0.000015)
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3327. feature "SLC35B3" (0.000015)
3328. feature "PARP16" (0.000015)
3329. feature "PML" (0.000015)
3330. feature "NAB2" (0.000015)
3331. feature "ZNF674" (0.000015)
3332. feature "HCN2" (0.000015)
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3334. feature "FLNC" (0.000015)
3335. feature "ALOX15" (0.000015)
3336. feature "C1QL1" (0.000015)
3337. feature "KCNMA1" (0.000015)
3338. feature "FCHO1" (0.000015)
3339. feature "OSBPL6" (0.000015)
3340. feature "CHMP6" (0.000015)
3341. feature "PHF19" (0.000015)
3342. feature "LINC00863" (0.000015)
3343. feature "ANKRD20A5P" (0.000015)
3344. feature "NOG" (0.000015)
3345. feature "DUSP9" (0.000015)
3346. feature "MLYCD" (0.000015)
3347. feature "STARD3" (0.000015)
3348. feature "DOCK8-AS1" (0.000015)
3349. feature "CRYBG2" (0.000015)
3350. feature "E2F2" (0.000015)
3351. feature "C12orf76" (0.000015)
3352. feature "BMS1P23" (0.000015)
3353. feature "SLC22A23" (0.000015)
3354. feature "RAB39B" (0.000015)
3355. feature "SMIM5" (0.000015)
3356. feature "UPRT" (0.000015)
3357. feature "PIK3IP1" (0.000015)
3358. feature "BMI1" (0.000015)
3359. feature "CRYBB2" (0.000015)
3360. feature "GLB1" (0.000015)
3361. feature "FBXO43" (0.000015)
3362. feature "KCNG1" (0.000015)
3363. feature "DNAJC6" (0.000015)
3364. feature "ABCA2" (0.000015)
3365. feature "PADI2" (0.000015)
3366. feature "EPHX2" (0.000015)
3367. feature "FIZ1" (0.000015)
3368. feature "DPY19L1P1" (0.000015)
3369. feature "OTULINL" (0.000015)
3370. feature "SYNPO2L" (0.000015)
3371. feature "HSP90AA2P" (0.000015)
3372. feature "N6AMT1" (0.000015)
3373. feature "AKR1B10" (0.000015)
3374. feature "IVL" (0.000015)
3375. feature "ABHD14B" (0.000015)
3376. feature "VASN" (0.000015)
3377. feature "RHOB" (0.000015)
3378. feature "SLC45A4" (0.000015)
3379. feature "ABCC13" (0.000015)
3380. feature "GRK4" (0.000015)
3381. feature "DSCR8" (0.000015)
3382. feature "PPP1R3B" (0.000015)
3383. feature "RTN4R" (0.000015)
3384. feature "GHDC" (0.000015)
3385. feature "FBXO38" (0.000015)
3386. feature "CYP27C1" (0.000015)
3387. feature "GRASLND" (0.000015)
3388. feature "TTLL1" (0.000015)
3389. feature "FRAS1" (0.000015)
3390. feature "HELQ" (0.000015)
3391. feature "DOK7" (0.000015)
3392. feature "MAP3K6" (0.000015)
3393. feature "CARF" (0.000015)
3394. feature "ASB13" (0.000015)
3395. feature "BHLHE40" (0.000015)
3396. feature "CSRNP1" (0.000015)
3397. feature "LAT" (0.000015)
3398. feature "KCTD17" (0.000015)
3399. feature "PCDHB9" (0.000015)
3400. feature "SBF1" (0.000015)
3401. feature "PBX3" (0.000015)
3402. feature "C8orf58" (0.000015)
3403. feature "FAM86EP" (0.000015)
3404. feature "AMZ1" (0.000014)
3405. feature "TMPRSS3" (0.000014)
3406. feature "CAPN5" (0.000014)
3407. feature "CCNB3P1" (0.000014)
3408. feature "MARS2" (0.000014)
3409. feature "CRTC2" (0.000014)
3410. feature "FOXD2" (0.000014)
3411. feature "ZCCHC14" (0.000014)
3412. feature "ITGB8" (0.000014)
3413. feature "FLVCR2" (0.000014)
3414. feature "ZFHX2" (0.000014)
3415. feature "ZNF514" (0.000014)
3416. feature "HLA-F" (0.000014)
3417. feature "BTBD7P1" (0.000014)
3418. feature "ZNF219" (0.000014)
3419. feature "LRRC8B" (0.000014)
3420. feature "SLC30A1" (0.000014)
3421. feature "CDK19" (0.000014)
3422. feature "RILPL2" (0.000014)
3423. feature "PCED1A" (0.000014)
3424. feature "ARPC1B" (0.000014)
3425. feature "CDX2" (0.000014)
3426. feature "PPP1R14C" (0.000014)
3427. feature "LRRC3" (0.000014)
3428. feature "SLC27A4" (0.000014)
3429. feature "LINC00680" (0.000014)
3430. feature "RPL12P13" (0.000014)
3431. feature "DNM3" (0.000014)
3432. feature "RFPL3S" (0.000014)
3433. feature "KCTD14" (0.000014)
3434. feature "MEMO1" (0.000014)
3435. feature "GOLT1A" (0.000014)
3436. feature "FHIP2B" (0.000014)
3437. feature "PLEKHF1" (0.000014)
3438. feature "SNX11" (0.000014)
3439. feature "SNAP25-AS1" (0.000014)
3440. feature "POC1A" (0.000014)
3441. feature "WDR6" (0.000014)
3442. feature "SLC2A13" (0.000014)
3443. feature "STAT2" (0.000014)
3444. feature "SEPTIN3" (0.000014)
3445. feature "PARD3B" (0.000014)
3446. feature "SHMT1" (0.000014)
3447. feature "RFNG" (0.000014)
3448. feature "RRP9" (0.000014)
3449. feature "TRIM11" (0.000014)
3450. feature "PCDHA10" (0.000014)
3451. feature "JADE1" (0.000014)
3452. feature "ZNF799" (0.000014)
3453. feature "PRELID3A" (0.000014)
3454. feature "COPG2IT1" (0.000014)
3455. feature "ID4" (0.000014)
3456. feature "ARHGEF2" (0.000014)
3457. feature "ZNF169" (0.000014)
3458. feature "HSF2BP" (0.000014)
3459. feature "TRAF6" (0.000014)
3460. feature "KLHL24" (0.000014)
3461. feature "TMEM178B" (0.000014)
3462. feature "GALNS" (0.000014)
3463. feature "TMEM52" (0.000014)
3464. feature "LINC02178" (0.000014)
3465. feature "FSTL1" (0.000014)
3466. feature "AAAS" (0.000014)
3467. feature "TBCCD1" (0.000014)
3468. feature "PLEKHG6" (0.000014)
3469. feature "STK16" (0.000014)
3470. feature "TTK" (0.000014)
3471. feature "ZNF879" (0.000014)
3472. feature "TSBP1-AS1" (0.000014)
3473. feature "CASP10" (0.000014)
3474. feature "ABHD14A" (0.000014)
3475. feature "LINC00345" (0.000014)
3476. feature "LINC01214" (0.000014)
3477. feature "H2AW" (0.000014)
3478. feature "LINC02428" (0.000014)
3479. feature "HMGN1P14" (0.000014)
3480. feature "EPB41L4A-AS1" (0.000014)
3481. feature "ASH1L-AS1" (0.000014)
3482. feature "TINF2" (0.000014)
3483. feature "TENT5C" (0.000014)
3484. feature "ILRUN-AS1" (0.000014)
3485. feature "OVOL2" (0.000014)
3486. feature "NRG4" (0.000014)
3487. feature "TCF15" (0.000014)
3488. feature "ZNF132-DT" (0.000014)
3489. feature "RNF215" (0.000014)
3490. feature "BCL2" (0.000014)
3491. feature "FES" (0.000014)
3492. feature "PROSER2" (0.000014)
3493. feature "FAM171A2" (0.000014)
3494. feature "IRF7" (0.000014)
3495. feature "TCL6" (0.000014)
3496. feature "DPPA4" (0.000014)
3497. feature "BORCS6" (0.000014)
3498. feature "SHF" (0.000014)
3499. feature "MAPK8" (0.000014)
3500. feature "PIGK" (0.000014)
3501. feature "ZNF550" (0.000014)
3502. feature "RBM38" (0.000014)
3503. feature "MAGED1" (0.000014)
3504. feature "ABCA7" (0.000014)
3505. feature "SLC66A1" (0.000014)
3506. feature "FAM120C" (0.000014)
3507. feature "CTHRC1" (0.000014)
3508. feature "GLMP" (0.000014)
3509. feature "ASPM" (0.000014)
3510. feature "WNT7A" (0.000014)
3511. feature "LINC02298" (0.000014)
3512. feature "LRP4" (0.000014)
3513. feature "NEU3" (0.000013)
3514. feature "ZNF263" (0.000013)
3515. feature "MSI1" (0.000013)
3516. feature "PLD1" (0.000013)
3517. feature "NPRL2" (0.000013)
3518. feature "DPY19L1" (0.000013)
3519. feature "METTL7A" (0.000013)
3520. feature "P3H1" (0.000013)
3521. feature "NCKIPSD" (0.000013)
3522. feature "SERPINE1" (0.000013)
3523. feature "SRRD" (0.000013)
3524. feature "B9D1" (0.000013)
3525. feature "ANLN" (0.000013)
3526. feature "LPP-AS2" (0.000013)
3527. feature "HCP5" (0.000013)
3528. feature "SQLE" (0.000013)
3529. feature "BRD2" (0.000013)
3530. feature "MBD1" (0.000013)
3531. feature "TMCC1-DT" (0.000013)
3532. feature "CCDC136" (0.000013)
3533. feature "PROC" (0.000013)
3534. feature "MON1B" (0.000013)
3535. feature "GIN1" (0.000013)
3536. feature "FN3K" (0.000013)
3537. feature "ZBED6CL" (0.000013)
3538. feature "CLEC16A" (0.000013)
3539. feature "CNIH2" (0.000013)
3540. feature "CCDC74A" (0.000013)
3541. feature "KCNV1" (0.000013)
3542. feature "PTPRF" (0.000013)
3543. feature "NMRK1" (0.000013)
3544. feature "TMEM167B-DT" (0.000013)
3545. feature "B4GALT7" (0.000013)
3546. feature "HFE" (0.000013)
3547. feature "SMIM20" (0.000013)
3548. feature "CRLS1" (0.000013)
3549. feature "SH3GL3" (0.000013)
3550. feature "SARS2" (0.000013)
3551. feature "CMTM3" (0.000013)
3552. feature "AGAP1-IT1" (0.000013)
3553. feature "ADA2" (0.000013)
3554. feature "PCDH10" (0.000013)
3555. feature "FOXF2" (0.000013)
3556. feature "LINC02076" (0.000013)
3557. feature "PEX11A" (0.000013)
3558. feature "LINC01559" (0.000013)
3559. feature "ZNF585B" (0.000013)
3560. feature "UCKL1" (0.000013)
3561. feature "OAS1" (0.000013)
3562. feature "ZNF23" (0.000013)
3563. feature "ST3GAL6" (0.000013)
3564. feature "IL17RC" (0.000013)
3565. feature "GCNT2" (0.000013)
3566. feature "NXPH4" (0.000013)
3567. feature "SOWAHC" (0.000013)
3568. feature "FAM118B" (0.000013)
3569. feature "WASH3P" (0.000013)
3570. feature "BAGE2" (0.000013)
3571. feature "MYL9" (0.000013)
3572. feature "HECTD3" (0.000013)
3573. feature "SCARA3" (0.000013)
3574. feature "VPS51" (0.000013)
3575. feature "SLC4A11" (0.000013)
3576. feature "AARSD1" (0.000013)
3577. feature "SLC9A3-AS1" (0.000013)
3578. feature "SDAD1P1" (0.000013)
3579. feature "GNGT1" (0.000013)
3580. feature "PCDHB4" (0.000013)
3581. feature "EPM2A" (0.000013)
3582. feature "SATL1" (0.000013)
3583. feature "SYTL3" (0.000013)
3584. feature "TIGD5" (0.000013)
3585. feature "TMEM182" (0.000013)
3586. feature "ZDHHC1" (0.000013)
3587. feature "PRORSD1P" (0.000013)
3588. feature "EGLN1" (0.000013)
3589. feature "H2AZ2P1" (0.000013)
3590. feature "ZNF202" (0.000013)
3591. feature "GVINP1" (0.000013)
3592. feature "ADPRM" (0.000013)
3593. feature "MUCL1" (0.000013)
3594. feature "CERK" (0.000013)
3595. feature "PHLDA3" (0.000013)
3596. feature "FBXL4" (0.000013)
3597. feature "BCDIN3D" (0.000013)
3598. feature "CCDC168" (0.000013)
3599. feature "FAM214B" (0.000013)
3600. feature "ATP9A" (0.000013)
3601. feature "SLC39A11" (0.000013)
3602. feature "KCNS3" (0.000013)
3603. feature "RTL10" (0.000013)
3604. feature "NRARP" (0.000013)
3605. feature "LINC01722" (0.000013)
3606. feature "GASK1B" (0.000013)
3607. feature "HEXD-IT1" (0.000013)
3608. feature "FAM234B" (0.000013)
3609. feature "MAP2K5" (0.000013)
3610. feature "CYP2C9" (0.000013)
3611. feature "ABHD17A" (0.000013)
3612. feature "ZNF510" (0.000013)
3613. feature "HS1BP3" (0.000013)
3614. feature "ZKSCAN2" (0.000013)
3615. feature "MPPE1" (0.000013)
3616. feature "CLCN7" (0.000013)
3617. feature "TRAF2" (0.000013)
3618. feature "MEX3A" (0.000013)
3619. feature "PHF1" (0.000013)
3620. feature "PELI3" (0.000013)
3621. feature "RNASEK" (0.000013)
3622. feature "FTHL17" (0.000013)
3623. feature "MESP1" (0.000013)
3624. feature "ZNF653" (0.000013)
3625. feature "HACD4" (0.000013)
3626. feature "EML1" (0.000013)
3627. feature "FLT3LG" (0.000012)
3628. feature "ZNF354B" (0.000012)
3629. feature "SH3RF2" (0.000012)
3630. feature "KCNF1" (0.000012)
3631. feature "ABHD15" (0.000012)
3632. feature "OR7E25P" (0.000012)
3633. feature "ARMC7" (0.000012)
3634. feature "MRPL38" (0.000012)
3635. feature "SUMF1" (0.000012)
3636. feature "ZBTB22" (0.000012)
3637. feature "ZBED6" (0.000012)
3638. feature "ARL10" (0.000012)
3639. feature "ARRDC2" (0.000012)
3640. feature "LINC01355" (0.000012)
3641. feature "RNF170" (0.000012)
3642. feature "LENG9" (0.000012)
3643. feature "B3GALT9" (0.000012)
3644. feature "CCDC120" (0.000012)
3645. feature "NEK3" (0.000012)
3646. feature "KREMEN2" (0.000012)
3647. feature "CHIC2" (0.000012)
3648. feature "NRF1" (0.000012)
3649. feature "DNHD1" (0.000012)
3650. feature "MKLN1-AS" (0.000012)
3651. feature "RAMP1" (0.000012)
3652. feature "TUBBP5" (0.000012)
3653. feature "GAB1" (0.000012)
3654. feature "EPOP" (0.000012)
3655. feature "MAD1L1" (0.000012)
3656. feature "RNF227" (0.000012)
3657. feature "GPR158" (0.000012)
3658. feature "ERP27" (0.000012)
3659. feature "LINC01695" (0.000012)
3660. feature "ZYG11A" (0.000012)
3661. feature "CAVIN2" (0.000012)
3662. feature "ACSF3" (0.000012)
3663. feature "RHOH" (0.000012)
3664. feature "ZNF836" (0.000012)
3665. feature "ARHGEF40" (0.000012)
3666. feature "ABTB2" (0.000012)
3667. feature "BMP1" (0.000012)
3668. feature "EPHX1" (0.000012)
3669. feature "NAT14" (0.000012)
3670. feature "NRBP2" (0.000012)
3671. feature "DAB2" (0.000012)
3672. feature "BCR" (0.000012)
3673. feature "CARD18" (0.000012)
3674. feature "SOX9-AS1" (0.000012)
3675. feature "ARSJ" (0.000012)
3676. feature "FAM166A" (0.000012)
3677. feature "ZNF132" (0.000012)
3678. feature "PAQR4" (0.000012)
3679. feature "ORMDL3" (0.000012)
3680. feature "ZNF579" (0.000012)
3681. feature "DUSP4" (0.000012)
3682. feature "GJD2" (0.000012)
3683. feature "SPX" (0.000012)
3684. feature "ZFP36L2" (0.000012)
3685. feature "ZFYVE28" (0.000012)
3686. feature "CLDN15" (0.000012)
3687. feature "TAFAZZIN" (0.000012)
3688. feature "FAM136BP" (0.000012)
3689. feature "ACOT2" (0.000012)
3690. feature "UCK1" (0.000012)
3691. feature "HBM" (0.000012)
3692. feature "ZNF354C" (0.000012)
3693. feature "PTPRB" (0.000012)
3694. feature "HOXA11-AS" (0.000012)
3695. feature "WASF3" (0.000012)
3696. feature "MARK4" (0.000012)
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3698. feature "MELTF" (0.000012)
3699. feature "ADGRF4" (0.000012)
3700. feature "BFSP1" (0.000012)
3701. feature "RAPGEF5" (0.000012)
3702. feature "TRMT61B" (0.000012)
3703. feature "ALDH5A1" (0.000012)
3704. feature "FCRL5" (0.000012)
3705. feature "SPATC1L" (0.000012)
3706. feature "ERAP2" (0.000012)
3707. feature "ANO7" (0.000012)
3708. feature "LINC02225" (0.000012)
3709. feature "HBP1" (0.000012)
3710. feature "PTPN6" (0.000012)
3711. feature "FSIP1" (0.000012)
3712. feature "HERC5" (0.000012)
3713. feature "WNT4" (0.000012)
3714. feature "INPP5A" (0.000011)
3715. feature "TRAPPC6A" (0.000011)
3716. feature "ARL9" (0.000011)
3717. feature "FHL3" (0.000011)
3718. feature "WDR53" (0.000011)
3719. feature "TMEM8B" (0.000011)
3720. feature "TXNDC15" (0.000011)
3721. feature "ZNF251" (0.000011)
3722. feature "TMTC2" (0.000011)
3723. feature "LINC02870" (0.000011)
3724. feature "MTFR1L" (0.000011)
3725. feature "RAET1E" (0.000011)
3726. feature "FAM110D" (0.000011)
3727. feature "CTH" (0.000011)
3728. feature "MIOS-DT" (0.000011)
3729. feature "DOP1B" (0.000011)
3730. feature "BCO2" (0.000011)
3731. feature "PANK1" (0.000011)
3732. feature "ORC1" (0.000011)
3733. feature "CTU2" (0.000011)
3734. feature "BMP4" (0.000011)
3735. feature "USP35" (0.000011)
3736. feature "MIR137HG" (0.000011)
3737. feature "RCCD1" (0.000011)
3738. feature "TP53INP2" (0.000011)
3739. feature "C7orf26" (0.000011)
3740. feature "SNORC" (0.000011)
3741. feature "DENND3" (0.000011)
3742. feature "SH3BP1" (0.000011)
3743. feature "CACHD1" (0.000011)
3744. feature "ACVR1C" (0.000011)
3745. feature "UGT1A7" (0.000011)
3746. feature "RPL13AP7" (0.000011)
3747. feature "VWA2" (0.000011)
3748. feature "TUBB3" (0.000011)
3749. feature "DENND5B" (0.000011)
3750. feature "B9D2" (0.000011)
3751. feature "KLHL21" (0.000011)
3752. feature "RGMB" (0.000011)
3753. feature "GRIN1" (0.000011)
3754. feature "TRPC3" (0.000011)
3755. feature "SLC3A2" (0.000011)
3756. feature "SGMS1-AS1" (0.000011)
3757. feature "ALX1" (0.000011)
3758. feature "TGDS" (0.000011)
3759. feature "FURIN" (0.000011)
3760. feature "ZBTB46" (0.000011)
3761. feature "GPRC5A" (0.000011)
3762. feature "NPHP4" (0.000011)
3763. feature "SCN9A" (0.000011)
3764. feature "CCDC191" (0.000011)
3765. feature "GPR137C" (0.000011)
3766. feature "GSEC" (0.000011)
3767. feature "A4GALT" (0.000011)
3768. feature "LINC00853" (0.000011)
3769. feature "LINC00343" (0.000011)
3770. feature "ZNF250" (0.000011)
3771. feature "P2RX7" (0.000011)
3772. feature "KIF12" (0.000011)
3773. feature "DMPK" (0.000011)
3774. feature "KLHDC3" (0.000011)
3775. feature "RHOQP1" (0.000011)
3776. feature "BRME1" (0.000011)
3777. feature "GREP1" (0.000011)
3778. feature "ANKRD44" (0.000011)
3779. feature "ZNF569" (0.000011)
3780. feature "MOB2" (0.000011)
3781. feature "NCAPG2" (0.000011)
3782. feature "RNF123" (0.000011)
3783. feature "MCM3" (0.000011)
3784. feature "HSPD1P11" (0.000011)
3785. feature "CHST5" (0.000011)
3786. feature "PIM1" (0.000011)
3787. feature "CRX" (0.000011)
3788. feature "FGD3" (0.000011)
3789. feature "RAB37" (0.000011)
3790. feature "LTB4R" (0.000011)
3791. feature "CPNE2" (0.000011)
3792. feature "FAM47E-STBD1" (0.000011)
3793. feature "TMPRSS2" (0.000011)
3794. feature "ITGA4" (0.000011)
3795. feature "FAM131A" (0.000011)
3796. feature "UBE2L6" (0.000011)
3797. feature "PANK4" (0.000011)
3798. feature "SOX9" (0.000011)
3799. feature "TMEM104" (0.000011)
3800. feature "IL20RA" (0.000011)
3801. feature "MFAP3" (0.000011)
3802. feature "ZNF702P" (0.000011)
3803. feature "NTN1" (0.000011)
3804. feature "TFEB" (0.000011)
3805. feature "FAM185BP" (0.000011)
3806. feature "LINC01607" (0.000011)
3807. feature "EXOSC10-AS1" (0.000011)
3808. feature "ZC3H10" (0.000011)
3809. feature "BOLA1" (0.000010)
3810. feature "KYAT3" (0.000010)
3811. feature "DLX3" (0.000010)
3812. feature "PCDHGA1" (0.000010)
3813. feature "RNF26" (0.000010)
3814. feature "ZNF747" (0.000010)
3815. feature "FBLN2" (0.000010)
3816. feature "BEND3" (0.000010)
3817. feature "STAM-DT" (0.000010)
3818. feature "OPN3" (0.000010)
3819. feature "ZBED5-AS1" (0.000010)
3820. feature "DXO" (0.000010)
3821. feature "DELE1" (0.000010)
3822. feature "USP30" (0.000010)
3823. feature "ANKRD45" (0.000010)
3824. feature "CTDP1" (0.000010)
3825. feature "SNX18" (0.000010)
3826. feature "ZNF558" (0.000010)
3827. feature "ARHGEF26-AS1" (0.000010)
3828. feature "TBC1D25" (0.000010)
3829. feature "SYDE1" (0.000010)
3830. feature "RHOXF2B" (0.000010)
3831. feature "ANKRA2" (0.000010)
3832. feature "MPP3" (0.000010)
3833. feature "PRDM11" (0.000010)
3834. feature "STON1" (0.000010)
3835. feature "ZNF418" (0.000010)
3836. feature "PRRG2" (0.000010)
3837. feature "CDAN1" (0.000010)
3838. feature "ZNF77" (0.000010)
3839. feature "TEP1" (0.000010)
3840. feature "LINC01725" (0.000010)
3841. feature "DGLUCY" (0.000010)
3842. feature "KCNC3" (0.000010)
3843. feature "CD55" (0.000010)
3844. feature "SLC22A5" (0.000010)
3845. feature "FAM43A" (0.000010)
3846. feature "CTTN-DT" (0.000010)
3847. feature "PPOX" (0.000010)
3848. feature "DNAAF9" (0.000010)
3849. feature "PSPN" (0.000010)
3850. feature "CATSPERB" (0.000010)
3851. feature "TNFRSF11B" (0.000010)
3852. feature "TPST1" (0.000010)
3853. feature "DARS1-AS1" (0.000010)
3854. feature "ZKSCAN3" (0.000010)
3855. feature "ANXA9" (0.000010)
3856. feature "SETD9" (0.000010)
3857. feature "OR2A5" (0.000010)
3858. feature "PLEKHG5" (0.000010)
3859. feature "PYY2" (0.000010)
3860. feature "MELTF-AS1" (0.000010)
3861. feature "TRPV4" (0.000010)
3862. feature "NTF4" (0.000010)
3863. feature "PLD6" (0.000010)
3864. feature "IDI1" (0.000010)
3865. feature "B3GALT1" (0.000010)
3866. feature "MST1" (0.000010)
3867. feature "HLA-DPB1" (0.000010)
3868. feature "PPP2R2B" (0.000010)
3869. feature "KLC3" (0.000010)
3870. feature "MDM1" (0.000010)
3871. feature "SLC37A2" (0.000010)
3872. feature "WHAMMP3" (0.000010)
3873. feature "XYLB" (0.000010)
3874. feature "STK36" (0.000010)
3875. feature "DIXDC1" (0.000010)
3876. feature "RHD" (0.000010)
3877. feature "DMTN" (0.000010)
3878. feature "NLRX1" (0.000010)
3879. feature "ZNF22" (0.000010)
3880. feature "CASC9" (0.000010)
3881. feature "KCNE4" (0.000010)
3882. feature "LRRC23" (0.000010)
3883. feature "FBXL17" (0.000010)
3884. feature "TTC38" (0.000010)
3885. feature "KITLG" (0.000010)
3886. feature "IL18BP" (0.000010)
3887. feature "INPP4A" (0.000010)
3888. feature "CSF1" (0.000010)
3889. feature "ATG9A" (0.000010)
3890. feature "IFITM10" (0.000010)
3891. feature "CMYA5" (0.000010)
3892. feature "RMI2" (0.000010)
3893. feature "C11orf98" (0.000010)
3894. feature "ZNF461" (0.000010)
3895. feature "ZNF90" (0.000010)
3896. feature "ATP6V1B2" (0.000010)
3897. feature "RHBDF1" (0.000010)
3898. feature "RECQL5" (0.000010)
3899. feature "MPP2" (0.000010)
3900. feature "EFCAB1" (0.000010)
3901. feature "WDR19" (0.000010)
3902. feature "PROS1" (0.000010)
3903. feature "HERPUD2-AS1" (0.000010)
3904. feature "MYD88" (0.000010)
3905. feature "LENG8-AS1" (0.000010)
3906. feature "LINC02273" (0.000010)
3907. feature "FKBP4" (0.000010)
3908. feature "DNAJC3-DT" (0.000010)
3909. feature "PGAP3" (0.000010)
3910. feature "FZD1" (0.000010)
3911. feature "ERG" (0.000010)
3912. feature "VDAC1P8" (0.000010)
3913. feature "DPY19L2P1" (0.000010)
3914. feature "C1orf210" (0.000010)
3915. feature "BAMBI" (0.000010)
3916. feature "MT-TT" (0.000010)
3917. feature "SSPN" (0.000010)
3918. feature "CTIF" (0.000010)
3919. feature "RNLS" (0.000010)
3920. feature "DIS3L" (0.000010)
3921. feature "HSBP1L1" (0.000010)
3922. feature "ANKRD34A" (0.000010)
3923. feature "ZNF850" (0.000010)
3924. feature "CDC42EP3" (0.000010)
3925. feature "FHAD1" (0.000010)
3926. feature "TK2" (0.000010)
3927. feature "DOK4" (0.000010)
3928. feature "ZNF772" (0.000010)
3929. feature "RPL10AP6" (0.000010)
3930. feature "PCDH11X" (0.000010)
3931. feature "ADNP-AS1" (0.000010)
3932. feature "RUBCN" (0.000010)
3933. feature "CELSR1" (0.000010)
3934. feature "MAFG" (0.000010)
3935. feature "SNX25" (0.000010)
3936. feature "SMOX" (0.000010)
3937. feature "SEC14L2" (0.000010)
3938. feature "SLC30A6-DT" (0.000010)
3939. feature "KIF9-AS1" (0.000010)
3940. feature "ADORA1" (0.000010)
3941. feature "WDR72" (0.000010)
3942. feature "DGKQ" (0.000010)
3943. feature "DTD2" (0.000009)
3944. feature "SYCE1L" (0.000009)
3945. feature "LINC00960" (0.000009)
3946. feature "HMGCR" (0.000009)
3947. feature "CLCN6" (0.000009)
3948. feature "PECR" (0.000009)
3949. feature "ZNF286B" (0.000009)
3950. feature "SORBS1" (0.000009)
3951. feature "KAT14" (0.000009)
3952. feature "GREB1" (0.000009)
3953. feature "KLK10" (0.000009)
3954. feature "LINC00997" (0.000009)
3955. feature "IL11" (0.000009)
3956. feature "LAX1" (0.000009)
3957. feature "BDKRB2" (0.000009)
3958. feature "TMEM62" (0.000009)
3959. feature "ALDH1A3-AS1" (0.000009)
3960. feature "HPSE" (0.000009)
3961. feature "PLXND1" (0.000009)
3962. feature "MCOLN1" (0.000009)
3963. feature "FSD1L" (0.000009)
3964. feature "KCNJ6" (0.000009)
3965. feature "DOCK8" (0.000009)
3966. feature "TNFRSF14" (0.000009)
3967. feature "MTMR1" (0.000009)
3968. feature "LINC00294" (0.000009)
3969. feature "CYP19A1" (0.000009)
3970. feature "SCD" (0.000009)
3971. feature "FBXL15" (0.000009)
3972. feature "PARM1" (0.000009)
3973. feature "CNKSR1" (0.000009)
3974. feature "RHOU" (0.000009)
3975. feature "MTCO1P40" (0.000009)
3976. feature "MCM10" (0.000009)
3977. feature "ITPRIP" (0.000009)
3978. feature "DIPK1A" (0.000009)
3979. feature "ZNF542P" (0.000009)
3980. feature "FBXO33" (0.000009)
3981. feature "NFKBIA" (0.000009)
3982. feature "SLC25A19" (0.000009)
3983. feature "NTRK2" (0.000009)
3984. feature "B4GALNT2" (0.000009)
3985. feature "CCT6B" (0.000009)
3986. feature "TVP23C" (0.000009)
3987. feature "S100A8" (0.000009)
3988. feature "EVI5L" (0.000009)
3989. feature "C19orf44" (0.000009)
3990. feature "C11orf71" (0.000009)
3991. feature "CDC42BPG" (0.000009)
3992. feature "KCNMB4" (0.000009)
3993. feature "ZNF530" (0.000009)
3994. feature "KDM4C" (0.000009)
3995. feature "ZNF710" (0.000009)
3996. feature "TMEM139" (0.000009)
3997. feature "TPH1" (0.000009)
3998. feature "IL1RAP" (0.000009)
3999. feature "HTR7P1" (0.000009)
4000. feature "GNAO1-DT" (0.000009)
4001. feature "PRAG1" (0.000009)
4002. feature "KRT83" (0.000009)
4003. feature "NARF-AS2" (0.000009)
4004. feature "ACTG2" (0.000009)
4005. feature "CAPRIN2" (0.000009)
4006. feature "NAIP" (0.000009)
4007. feature "TM4SF19-AS1" (0.000009)
4008. feature "SLC19A2" (0.000009)
4009. feature "ZNF391" (0.000009)
4010. feature "NUDT16L2P" (0.000009)
4011. feature "PCDHGB6" (0.000009)
4012. feature "F8" (0.000009)
4013. feature "MYLK-AS1" (0.000009)
4014. feature "PPP1R3C" (0.000009)
4015. feature "PLCD4" (0.000009)
4016. feature "NDN" (0.000009)
4017. feature "C12orf56" (0.000009)
4018. feature "C2CD2" (0.000009)
4019. feature "LY6E-DT" (0.000009)
4020. feature "PID1" (0.000009)
4021. feature "YRDC" (0.000009)
4022. feature "VWA1" (0.000009)
4023. feature "PBLD" (0.000009)
4024. feature "KDM5B" (0.000009)
4025. feature "SLC24A2" (0.000009)
4026. feature "TMEM158" (0.000009)
4027. feature "ZNF319" (0.000009)
4028. feature "SLC25A51P4" (0.000009)
4029. feature "TXNDC5" (0.000009)
4030. feature "CALML3-AS1" (0.000009)
4031. feature "ZNF45" (0.000009)
4032. feature "FOXE1" (0.000009)
4033. feature "ZNF572" (0.000009)
4034. feature "ABAT" (0.000009)
4035. feature "BMP2" (0.000009)
4036. feature "OSER1-DT" (0.000009)
4037. feature "GYG2" (0.000009)
4038. feature "SHFL" (0.000009)
4039. feature "FAM81A" (0.000009)
4040. feature "ACP5" (0.000009)
4041. feature "EVC2" (0.000009)
4042. feature "FOS" (0.000009)
4043. feature "BTG2" (0.000009)
4044. feature "LINC01902" (0.000009)
4045. feature "PIGBOS1" (0.000009)
4046. feature "NFKBID" (0.000009)
4047. feature "NUPR2" (0.000009)
4048. feature "PTCH1" (0.000009)
4049. feature "CTSK" (0.000009)
4050. feature "GNAS-AS1" (0.000009)
4051. feature "ATL1" (0.000009)
4052. feature "ANO7L1" (0.000009)
4053. feature "MAP3K12" (0.000009)
4054. feature "ZKSCAN4" (0.000009)
4055. feature "INKA1" (0.000009)
4056. feature "ZNF383" (0.000009)
4057. feature "DTL" (0.000009)
4058. feature "ITGA6" (0.000009)
4059. feature "FLI1" (0.000009)
4060. feature "ASAH2B" (0.000009)
4061. feature "ZNF416" (0.000009)
4062. feature "STARD4" (0.000009)
4063. feature "HDAC5" (0.000009)
4064. feature "ZNF90P3" (0.000009)
4065. feature "MYO3B-AS1" (0.000009)
4066. feature "RETSAT" (0.000009)
4067. feature "HDAC11" (0.000009)
4068. feature "NPL" (0.000009)
4069. feature "ANKRD13B" (0.000009)
4070. feature "TSHR" (0.000009)
4071. feature "LYSMD1" (0.000009)
4072. feature "ADCY2" (0.000009)
4073. feature "GSDMB" (0.000009)
4074. feature "UPF3AP2" (0.000009)
4075. feature "MRPL20-AS1" (0.000009)
4076. feature "SOX7" (0.000009)
4077. feature "PHC2" (0.000008)
4078. feature "EHHADH" (0.000008)
4079. feature "DPH1" (0.000008)
4080. feature "TMEM218" (0.000008)
4081. feature "MYG1-AS1" (0.000008)
4082. feature "SSR3" (0.000008)
4083. feature "MICB" (0.000008)
4084. feature "C19orf48" (0.000008)
4085. feature "ADGRE1" (0.000008)
4086. feature "G0S2" (0.000008)
4087. feature "POMGNT2" (0.000008)
4088. feature "TTC30A" (0.000008)
4089. feature "ZNF257" (0.000008)
4090. feature "LIMD2" (0.000008)
4091. feature "CPE" (0.000008)
4092. feature "IFIT5" (0.000008)
4093. feature "TMEM191C" (0.000008)
4094. feature "SNURF" (0.000008)
4095. feature "TPRG1" (0.000008)
4096. feature "GPAT3" (0.000008)
4097. feature "STIM2-AS1" (0.000008)
4098. feature "NLRP10" (0.000008)
4099. feature "DNAI4" (0.000008)
4100. feature "ZCCHC24" (0.000008)
4101. feature "ACTG1P25" (0.000008)
4102. feature "TUBG1" (0.000008)
4103. feature "NAT1" (0.000008)
4104. feature "OSGEPL1" (0.000008)
4105. feature "PRDM1" (0.000008)
4106. feature "HMBOX1" (0.000008)
4107. feature "EMP3" (0.000008)
4108. feature "CLHC1" (0.000008)
4109. feature "D2HGDH" (0.000008)
4110. feature "CERS4" (0.000008)
4111. feature "HHEX" (0.000008)
4112. feature "SLC16A4" (0.000008)
4113. feature "CTSD" (0.000008)
4114. feature "TAF6L" (0.000008)
4115. feature "CDKL3" (0.000008)
4116. feature "EDAR" (0.000008)
4117. feature "NRXN3" (0.000008)
4118. feature "VPS45" (0.000008)
4119. feature "SCRG1" (0.000008)
4120. feature "SHOX2" (0.000008)
4121. feature "IL20RB" (0.000008)
4122. feature "EGFLAM" (0.000008)
4123. feature "ZNF304" (0.000008)
4124. feature "HS3ST3B1" (0.000008)
4125. feature "SLC25A21-AS1" (0.000008)
4126. feature "ATP13A4" (0.000008)
4127. feature "FGF8" (0.000008)
4128. feature "STOX1" (0.000008)
4129. feature "TRIB2" (0.000008)
4130. feature "MTND4P12" (0.000008)
4131. feature "RPS12P16" (0.000008)
4132. feature "STON2" (0.000008)
4133. feature "FBXO4" (0.000008)
4134. feature "B3GLCT" (0.000008)
4135. feature "HSPD1P5" (0.000008)
4136. feature "TRIM68" (0.000008)
4137. feature "MOSPD3" (0.000008)
4138. feature "SUSD3" (0.000008)
4139. feature "UCHL1" (0.000008)
4140. feature "TMEM216" (0.000008)
4141. feature "CHAC2" (0.000008)
4142. feature "PLAC9P1" (0.000008)
4143. feature "BRINP2" (0.000008)
4144. feature "FAM86C2P" (0.000008)
4145. feature "OR5AS1" (0.000008)
4146. feature "SLC6A9" (0.000008)
4147. feature "RABGGTA" (0.000008)
4148. feature "ACSS2" (0.000008)
4149. feature "RPL36AP26" (0.000008)
4150. feature "SLC30A2" (0.000008)
4151. feature "HBA1" (0.000008)
4152. feature "R3HDM4" (0.000008)
4153. feature "LINC01087" (0.000008)
4154. feature "HMGN1P26" (0.000008)
4155. feature "GBA" (0.000008)
4156. feature "PNPLA3" (0.000008)
4157. feature "NPTXR" (0.000008)
4158. feature "BEST1" (0.000008)
4159. feature "CDH8" (0.000008)
4160. feature "SH3D21" (0.000008)
4161. feature "TAGAP-AS1" (0.000008)
4162. feature "SAMD10" (0.000008)
4163. feature "ZNF826P" (0.000008)
4164. feature "DNAJC25-GNG10" (0.000008)
4165. feature "RPS18P9" (0.000008)
4166. feature "MYO18B" (0.000008)
4167. feature "RHOBTB1" (0.000008)
4168. feature "LCAT" (0.000008)
4169. feature "RPL34P1" (0.000008)
4170. feature "HDHD5-AS1" (0.000008)
4171. feature "RBBP8NL" (0.000008)
4172. feature "GPR85" (0.000008)
4173. feature "IQCC" (0.000008)
4174. feature "PRR14" (0.000008)
4175. feature "CENPBD1" (0.000008)
4176. feature "SLC2A3" (0.000008)
4177. feature "FADS1" (0.000008)
4178. feature "CSGALNACT1" (0.000008)
4179. feature "YPEL3" (0.000008)
4180. feature "MT-TI" (0.000008)
4181. feature "SGK1" (0.000008)
4182. feature "IRS3P" (0.000008)
4183. feature "CRTC1" (0.000008)
4184. feature "MIR3677HG" (0.000008)
4185. feature "XKR4" (0.000008)
4186. feature "LY6G5C" (0.000008)
4187. feature "MSMO1" (0.000008)
4188. feature "OBSL1" (0.000008)
4189. feature "TAP1" (0.000008)
4190. feature "GARNL3" (0.000008)
4191. feature "OR2L2" (0.000008)
4192. feature "PSKH1" (0.000008)
4193. feature "TESK1" (0.000008)
4194. feature "XDH" (0.000008)
4195. feature "DNAAF11" (0.000008)
4196. feature "CDKN2D" (0.000008)
4197. feature "NECTIN4" (0.000008)
4198. feature "PECAM1" (0.000008)
4199. feature "CHST14" (0.000008)
4200. feature "PITPNM2" (0.000008)
4201. feature "ANKRD9" (0.000008)
4202. feature "MVP" (0.000008)
4203. feature "ARHGEF19" (0.000008)
4204. feature "DTX2" (0.000008)
4205. feature "VASH2" (0.000008)
4206. feature "FAM72D" (0.000008)
4207. feature "FRMD3" (0.000007)
4208. feature "C17orf99" (0.000007)
4209. feature "CLIC3" (0.000007)
4210. feature "ANKRD42" (0.000007)
4211. feature "LINC02245" (0.000007)
4212. feature "CD59" (0.000007)
4213. feature "SLC31A2" (0.000007)
4214. feature "LINC01322" (0.000007)
4215. feature "LINC00513" (0.000007)
4216. feature "PLEKHM3" (0.000007)
4217. feature "CYRIA" (0.000007)
4218. feature "RTEL1-TNFRSF6B" (0.000007)
4219. feature "ZNF470" (0.000007)
4220. feature "KRT18P3" (0.000007)
4221. feature "EEPD1" (0.000007)
4222. feature "ZFP82" (0.000007)
4223. feature "AXIN2" (0.000007)
4224. feature "RAPGEF6" (0.000007)
4225. feature "MTCL1" (0.000007)
4226. feature "KRT86" (0.000007)
4227. feature "ALDH3A1" (0.000007)
4228. feature "IKBKE" (0.000007)
4229. feature "NR2F2-AS1" (0.000007)
4230. feature "STAMBPL1" (0.000007)
4231. feature "COL6A1" (0.000007)
4232. feature "ABCD1" (0.000007)
4233. feature "TRAPPC13" (0.000007)
4234. feature "PKD1L1" (0.000007)
4235. feature "RN7SL471P" (0.000007)
4236. feature "TMEM144" (0.000007)
4237. feature "U2AF1L4" (0.000007)
4238. feature "KLK6" (0.000007)
4239. feature "L3MBTL2-AS1" (0.000007)
4240. feature "EFCAB5" (0.000007)
4241. feature "RPL37P6" (0.000007)
4242. feature "PELI1" (0.000007)
4243. feature "ZNF195" (0.000007)
4244. feature "USP46-DT" (0.000007)
4245. feature "EDA2R" (0.000007)
4246. feature "TBC1D19" (0.000007)
4247. feature "SLC35C1" (0.000007)
4248. feature "OR4F13P" (0.000007)
4249. feature "TMEM179B" (0.000007)
4250. feature "PINK1" (0.000007)
4251. feature "DOCK6" (0.000007)
4252. feature "DHRS3" (0.000007)
4253. feature "CEP68" (0.000007)
4254. feature "INTS9" (0.000007)
4255. feature "LINC02541" (0.000007)
4256. feature "ITGA11" (0.000007)
4257. feature "ARL15" (0.000007)
4258. feature "RGS4" (0.000007)
4259. feature "CLEC2D" (0.000007)
4260. feature "NAP1L1P1" (0.000007)
4261. feature "DENND6B" (0.000007)
4262. feature "CCSER1" (0.000007)
4263. feature "DEGS2" (0.000007)
4264. feature "NRG1" (0.000007)
4265. feature "TMEM231" (0.000007)
4266. feature "ABHD16A" (0.000007)
4267. feature "PCGF2" (0.000007)
4268. feature "PTGES" (0.000007)
4269. feature "HYAL3" (0.000007)
4270. feature "IRAG1" (0.000007)
4271. feature "FAM166C" (0.000007)
4272. feature "MSRB3" (0.000007)
4273. feature "KRT6A" (0.000007)
4274. feature "DENND2D" (0.000007)
4275. feature "PYCR2" (0.000007)
4276. feature "SERPINB9" (0.000007)
4277. feature "SPINK5" (0.000007)
4278. feature "NOL6" (0.000007)
4279. feature "KIAA0895" (0.000007)
4280. feature "PRR19" (0.000007)
4281. feature "ASB7" (0.000007)
4282. feature "LHX6" (0.000007)
4283. feature "KANK1" (0.000007)
4284. feature "C17orf100" (0.000007)
4285. feature "ATP2A1-AS1" (0.000007)
4286. feature "RNF207" (0.000007)
4287. feature "ZNF182" (0.000007)
4288. feature "UNC13B" (0.000007)
4289. feature "RAB11FIP1P1" (0.000007)
4290. feature "PRRX2" (0.000007)
4291. feature "CHST6" (0.000007)
4292. feature "HDAC6" (0.000007)
4293. feature "METAP1D" (0.000007)
4294. feature "CCDC51" (0.000007)
4295. feature "PRRT2" (0.000007)
4296. feature "SDCBP2-AS1" (0.000007)
4297. feature "TTPA" (0.000007)
4298. feature "SNORA11F" (0.000007)
4299. feature "LINC01426" (0.000007)
4300. feature "TMEM147-AS1" (0.000007)
4301. feature "ZNRF2" (0.000007)
4302. feature "TRIT1" (0.000007)
4303. feature "C3orf35" (0.000007)
4304. feature "PINLYP" (0.000007)
4305. feature "RFTN1" (0.000007)
4306. feature "SIM2" (0.000007)
4307. feature "NRXN2" (0.000007)
4308. feature "TMCO6" (0.000007)
4309. feature "CCNE1" (0.000007)
4310. feature "DSTNP2" (0.000007)
4311. feature "ABHD3" (0.000007)
4312. feature "CLYBL" (0.000007)
4313. feature "ANKS6" (0.000007)
4314. feature "SLC8A1" (0.000007)
4315. feature "S100A9" (0.000007)
4316. feature "TMEM115" (0.000007)
4317. feature "PLEKHN1" (0.000007)
4318. feature "SLC38A2" (0.000007)
4319. feature "GASK1B-AS1" (0.000007)
4320. feature "ACADS" (0.000007)
4321. feature "LINC00598" (0.000007)
4322. feature "B3GNT7" (0.000007)
4323. feature "ZNF441" (0.000007)
4324. feature "HMGB1P1" (0.000007)
4325. feature "TP53I13" (0.000007)
4326. feature "SLC43A3" (0.000007)
4327. feature "ROBO1" (0.000007)
4328. feature "PLEKHA8P1" (0.000007)
4329. feature "CSPG4P12" (0.000007)
4330. feature "ZNF341" (0.000007)
4331. feature "ABCA3" (0.000007)
4332. feature "ADAP2" (0.000007)
4333. feature "PRECSIT" (0.000007)
4334. feature "DPRX" (0.000007)
4335. feature "CRB3" (0.000007)
4336. feature "MEX3C" (0.000007)
4337. feature "NEBL-AS1" (0.000007)
4338. feature "AHCYL2" (0.000007)
4339. feature "CNEP1R1" (0.000007)
4340. feature "SLC20A1" (0.000007)
4341. feature "HOXA7" (0.000007)
4342. feature "ZIC5" (0.000007)
4343. feature "PLAGL1" (0.000007)
4344. feature "CCDC40" (0.000007)
4345. feature "PDK2" (0.000007)
4346. feature "RAB11B-AS1" (0.000007)
4347. feature "ZNF213" (0.000007)
4348. feature "TLL1" (0.000007)
4349. feature "SARS1" (0.000007)
4350. feature "DNAJC28" (0.000007)
4351. feature "FGD2" (0.000007)
4352. feature "IYD" (0.000007)
4353. feature "FAM89A" (0.000007)
4354. feature "ITGB2-AS1" (0.000007)
4355. feature "PTPN7" (0.000007)
4356. feature "ZNF969P" (0.000007)
4357. feature "TFPI2-DT" (0.000007)
4358. feature "LINC02664" (0.000007)
4359. feature "YBEY" (0.000007)
4360. feature "PLXNA3" (0.000007)
4361. feature "DDX58" (0.000007)
4362. feature "EXPH5" (0.000007)
4363. feature "ARL14" (0.000007)
4364. feature "CAPN10-DT" (0.000007)
4365. feature "TDRKH-AS1" (0.000007)
4366. feature "IL1RAPL1" (0.000006)
4367. feature "GLB1L" (0.000006)
4368. feature "HDAC4" (0.000006)
4369. feature "CALCOCO1" (0.000006)
4370. feature "PTPN9" (0.000006)
4371. feature "RBM26-AS1" (0.000006)
4372. feature "NR1D1" (0.000006)
4373. feature "C3orf62" (0.000006)
4374. feature "HTR7" (0.000006)
4375. feature "RPL23AP7" (0.000006)
4376. feature "LINC00342" (0.000006)
4377. feature "DUSP12" (0.000006)
4378. feature "GIHCG" (0.000006)
4379. feature "COL19A1" (0.000006)
4380. feature "CDH1" (0.000006)
4381. feature "B3GNT5" (0.000006)
4382. feature "AKR1B1" (0.000006)
4383. feature "LINC01615" (0.000006)
4384. feature "SLC46A1" (0.000006)
4385. feature "HSH2D" (0.000006)
4386. feature "GAREM2" (0.000006)
4387. feature "MAGEA12" (0.000006)
4388. feature "PLCH1" (0.000006)
4389. feature "ANTXR2" (0.000006)
4390. feature "EBP" (0.000006)
4391. feature "TMEM39A" (0.000006)
4392. feature "SMCO4" (0.000006)
4393. feature "C5orf64" (0.000006)
4394. feature "INSYN2B" (0.000006)
4395. feature "DNAAF8" (0.000006)
4396. feature "TRIM45" (0.000006)
4397. feature "COMMD10" (0.000006)
4398. feature "C8orf88" (0.000006)
4399. feature "CHMP1B2P" (0.000006)
4400. feature "KLRK1-AS1" (0.000006)
4401. feature "RASSF10" (0.000006)
4402. feature "LINC01854" (0.000006)
4403. feature "ABTB1" (0.000006)
4404. feature "TRPT1" (0.000006)
4405. feature "ZNF117" (0.000006)
4406. feature "OR7E122P" (0.000006)
4407. feature "NXT2" (0.000006)
4408. feature "TEAD2" (0.000006)
4409. feature "PPIEL" (0.000006)
4410. feature "MMP1" (0.000006)
4411. feature "CFH" (0.000006)
4412. feature "ADGRL2" (0.000006)
4413. feature "KLK5" (0.000006)
4414. feature "ZNF880" (0.000006)
4415. feature "TRANK1" (0.000006)
4416. feature "PRIMPOL" (0.000006)
4417. feature "MFSD4A" (0.000006)
4418. feature "NT5E" (0.000006)
4419. feature "APOL3" (0.000006)
4420. feature "DENND4A" (0.000006)
4421. feature "AGO4" (0.000006)
4422. feature "ZNF555" (0.000006)
4423. feature "TCP11L1" (0.000006)
4424. feature "RINL" (0.000006)
4425. feature "UNC13C" (0.000006)
4426. feature "B4GAT1" (0.000006)
4427. feature "PPCDC" (0.000006)
4428. feature "SLC29A1" (0.000006)
4429. feature "MIF4GD-DT" (0.000006)
4430. feature "FXYD3" (0.000006)
4431. feature "CCDC153" (0.000006)
4432. feature "WWC2" (0.000006)
4433. feature "ZG16B" (0.000006)
4434. feature "TAB1" (0.000006)
4435. feature "KCNK5" (0.000006)
4436. feature "ZNF25" (0.000006)
4437. feature "PURPL" (0.000006)
4438. feature "NAALAD2" (0.000006)
4439. feature "KRT3" (0.000006)
4440. feature "MTUS2" (0.000006)
4441. feature "NEK6" (0.000006)
4442. feature "BBS7-DT" (0.000006)
4443. feature "C12orf50" (0.000006)
4444. feature "CDCA3" (0.000006)
4445. feature "RPL21P10" (0.000006)
4446. feature "DLX6-AS1" (0.000006)
4447. feature "STAR" (0.000006)
4448. feature "EXOC3-AS1" (0.000006)
4449. feature "VEGFA" (0.000006)
4450. feature "TRDN-AS1" (0.000006)
4451. feature "HHLA1" (0.000006)
4452. feature "GAB2" (0.000006)
4453. feature "RABGGTB" (0.000006)
4454. feature "BICDL2" (0.000006)
4455. feature "GIPR" (0.000006)
4456. feature "MOB1B" (0.000006)
4457. feature "FRG1JP" (0.000006)
4458. feature "NKX2-8" (0.000006)
4459. feature "ZP3" (0.000006)
4460. feature "PORCN" (0.000006)
4461. feature "PCSK5" (0.000006)
4462. feature "NR2F1-AS1" (0.000006)
4463. feature "KRT18P59" (0.000006)
4464. feature "SLC27A1" (0.000006)
4465. feature "IL1A" (0.000006)
4466. feature "SIRT3" (0.000006)
4467. feature "TRPV3" (0.000006)
4468. feature "CCDC9B" (0.000006)
4469. feature "LINC00667" (0.000006)
4470. feature "TRPM2-AS" (0.000006)
4471. feature "PEBP1" (0.000006)
4472. feature "PIEZO1" (0.000006)
4473. feature "KATNAL2" (0.000006)
4474. feature "ARTN" (0.000006)
4475. feature "MAGEA10" (0.000006)
4476. feature "TMEM128" (0.000006)
4477. feature "CYB561A3" (0.000006)
4478. feature "NFATC2" (0.000006)
4479. feature "ZNF704" (0.000006)
4480. feature "FER1L5" (0.000006)
4481. feature "INA" (0.000006)
4482. feature "ABO" (0.000006)
4483. feature "MIRLET7BHG" (0.000006)
4484. feature "CFAP410" (0.000006)
4485. feature "DUSP7" (0.000006)
4486. feature "TLR2" (0.000006)
4487. feature "LTBP2" (0.000006)
4488. feature "LINC00958" (0.000006)
4489. feature "PLCG2" (0.000006)
4490. feature "LINC02018" (0.000006)
4491. feature "EDEM1" (0.000006)
4492. feature "CHMP7" (0.000006)
4493. feature "C2orf42" (0.000006)
4494. feature "TUBA1A" (0.000006)
4495. feature "UBB" (0.000006)
4496. feature "REEP1" (0.000006)
4497. feature "UGT1A6" (0.000006)
4498. feature "LIX1L" (0.000006)
4499. feature "ANXA10" (0.000006)
4500. feature "RSAD1" (0.000006)
4501. feature "ZNF76" (0.000006)
4502. feature "TMEM79" (0.000006)
4503. feature "RNVU1-30" (0.000006)
4504. feature "HECW2" (0.000006)
4505. feature "KLF16" (0.000006)
4506. feature "MPRIP-AS1" (0.000006)
4507. feature "CLIP2" (0.000006)
4508. feature "PLA2G6" (0.000006)
4509. feature "PGBD5" (0.000006)
4510. feature "CACNB4" (0.000006)
4511. feature "ZNF615" (0.000006)
4512. feature "LRFN3" (0.000006)
4513. feature "RASGRP2" (0.000006)
4514. feature "NLRC5" (0.000006)
4515. feature "DISC1" (0.000006)
4516. feature "ZNF460" (0.000006)
4517. feature "HARBI1" (0.000006)
4518. feature "ACTN1" (0.000005)
4519. feature "MT1G" (0.000005)
4520. feature "GOLGA6L2" (0.000005)
4521. feature "CCNT2-AS1" (0.000005)
4522. feature "DENND4B" (0.000005)
4523. feature "PRKG1-AS1" (0.000005)
4524. feature "SLC9A3R1-AS1" (0.000005)
4525. feature "TBC1D27P" (0.000005)
4526. feature "MARCKSL1P2" (0.000005)
4527. feature "VAX2" (0.000005)
4528. feature "UBQLN1-AS1" (0.000005)
4529. feature "PDLIM2" (0.000005)
4530. feature "BRSK2" (0.000005)
4531. feature "HHAT" (0.000005)
4532. feature "CAPN13" (0.000005)
4533. feature "ARHGEF10L" (0.000005)
4534. feature "CA4" (0.000005)
4535. feature "LINC01962" (0.000005)
4536. feature "TSPAN33" (0.000005)
4537. feature "EEF1A1P25" (0.000005)
4538. feature "DTWD2" (0.000005)
4539. feature "MMAA" (0.000005)
4540. feature "CCDC141" (0.000005)
4541. feature "ARSL" (0.000005)
4542. feature "RPL5P3" (0.000005)
4543. feature "ZNF687-AS1" (0.000005)
4544. feature "FIBCD1" (0.000005)
4545. feature "STARD13-AS" (0.000005)
4546. feature "CKMT1A" (0.000005)
4547. feature "WDR73" (0.000005)
4548. feature "GALNT18" (0.000005)
4549. feature "SCX" (0.000005)
4550. feature "ZNF512" (0.000005)
4551. feature "ST20-AS1" (0.000005)
4552. feature "SLC36A2" (0.000005)
4553. feature "CPEB2" (0.000005)
4554. feature "OR7E14P" (0.000005)
4555. feature "XAF1" (0.000005)
4556. feature "TTYH1" (0.000005)
4557. feature "JHY" (0.000005)
4558. feature "MORN3" (0.000005)
4559. feature "GPR137B" (0.000005)
4560. feature "LBHD2" (0.000005)
4561. feature "CALR4P" (0.000005)
4562. feature "PPIAP46" (0.000005)
4563. feature "MMP28" (0.000005)
4564. feature "CNPY2-AS1" (0.000005)
4565. feature "NR2F2" (0.000005)
4566. feature "WASH6P" (0.000005)
4567. feature "NGEF" (0.000005)
4568. feature "UHRF1" (0.000005)
4569. feature "C20orf204" (0.000005)
4570. feature "LRRC56" (0.000005)
4571. feature "KLHL13" (0.000005)
4572. feature "TUSC1" (0.000005)
4573. feature "CDK6-AS1" (0.000005)
4574. feature "CRACR2B" (0.000005)
4575. feature "MPC1-DT" (0.000005)
4576. feature "CORO1A" (0.000005)
4577. feature "MCM3AP-AS1" (0.000005)
4578. feature "PHETA1" (0.000005)
4579. feature "NCF2" (0.000005)
4580. feature "PLA2G4B" (0.000005)
4581. feature "INMT" (0.000005)
4582. feature "LRFN1" (0.000005)
4583. feature "RBMS3" (0.000005)
4584. feature "SMPD4BP" (0.000005)
4585. feature "LINC00271" (0.000005)
4586. feature "LRRC2" (0.000005)
4587. feature "RCOR2" (0.000005)
4588. feature "PRKAA2" (0.000005)
4589. feature "NPIPB4" (0.000005)
4590. feature "ZNF212" (0.000005)
4591. feature "PINK1-AS" (0.000005)
4592. feature "MUC4" (0.000005)
4593. feature "MAPK8IP1" (0.000005)
4594. feature "CCDC69" (0.000005)
4595. feature "PGPEP1" (0.000005)
4596. feature "DLGAP1-AS2" (0.000005)
4597. feature "MIF-AS1" (0.000005)
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4640. feature "FOXD1" (0.000005)
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4734. feature "CCPG1" (0.000004)
4735. feature "COPZ2" (0.000004)
4736. feature "RNF185" (0.000004)
4737. feature "ZNF573" (0.000004)
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4739. feature "TCAIM" (0.000004)
4740. feature "SHISA4" (0.000004)
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4745. feature "RAPGEF3" (0.000004)
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4747. feature "TDRD5" (0.000004)
4748. feature "DLG4" (0.000004)
4749. feature "CTSH" (0.000004)
4750. feature "MIF" (0.000004)
4751. feature "NDRG4" (0.000004)
4752. feature "SLAMF9" (0.000004)
4753. feature "BMF" (0.000004)
4754. feature "PTGES3L" (0.000004)
4755. feature "XKR6" (0.000004)
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4757. feature "PPP3CC" (0.000004)
4758. feature "LINC00167" (0.000004)
4759. feature "WDR35" (0.000004)
4760. feature "CCDC183" (0.000004)
4761. feature "ZNF626" (0.000004)
4762. feature "CABP4" (0.000004)
4763. feature "PAX6" (0.000004)
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4765. feature "TM4SF1" (0.000004)
4766. feature "SH2B3" (0.000004)
4767. feature "IMMP2L" (0.000004)
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4770. feature "MC2R" (0.000004)
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4781. feature "RCCD1-AS1" (0.000004)
4782. feature "CNIH3" (0.000004)
4783. feature "LINC01764" (0.000004)
4784. feature "MTND2P28" (0.000004)
4785. feature "PARVB" (0.000004)
4786. feature "NME3" (0.000004)
4787. feature "BMS1P22" (0.000004)
4788. feature "IFRD1" (0.000004)
4789. feature "MAS1" (0.000004)
4790. feature "ZNF154" (0.000004)
4791. feature "PDLIM3" (0.000004)
4792. feature "CRYM-AS1" (0.000004)
4793. feature "ANK1" (0.000004)
4794. feature "SREBF2" (0.000004)
4795. feature "FAM228B" (0.000004)
4796. feature "ZNF417" (0.000004)
4797. feature "RSKR" (0.000004)
4798. feature "KRBOX4" (0.000004)
4799. feature "MYLPF" (0.000004)
4800. feature "RAB9A" (0.000004)
4801. feature "PDE9A" (0.000004)
4802. feature "SLC38A4" (0.000004)
4803. feature "ETS1" (0.000004)
4804. feature "C20orf96" (0.000004)
4805. feature "ATP1B2" (0.000004)
4806. feature "SLC4A10" (0.000004)
4807. feature "CNFN" (0.000004)
4808. feature "TMEM63C" (0.000004)
4809. feature "ZNF420" (0.000004)
4810. feature "FBXO17" (0.000004)
4811. feature "RDH14" (0.000004)
4812. feature "DNLZ" (0.000004)
4813. feature "TPCN2" (0.000004)
4814. feature "LPAR5" (0.000004)
4815. feature "GBP1" (0.000004)
4816. feature "SNORA28" (0.000004)
4817. feature "RAMP3" (0.000004)
4818. feature "SNN" (0.000004)
4819. feature "LINC01551" (0.000004)
4820. feature "LINC01560" (0.000004)
4821. feature "SETMAR" (0.000004)
4822. feature "CHRNG" (0.000004)
4823. feature "SOX17" (0.000004)
4824. feature "CCDC15-DT" (0.000004)
4825. feature "PPFIA3" (0.000004)
4826. feature "SF3A2" (0.000004)
4827. feature "GOLGA2P5" (0.000004)
4828. feature "TNC" (0.000004)
4829. feature "VPS37D" (0.000004)
4830. feature "RASAL1" (0.000004)
4831. feature "FOXO6" (0.000004)
4832. feature "MCMDC2" (0.000004)
4833. feature "MYO18A" (0.000004)
4834. feature "SLC9A3" (0.000004)
4835. feature "PAQR9-AS1" (0.000004)
4836. feature "OTUB2" (0.000004)
4837. feature "FHIP1A" (0.000004)
4838. feature "TRIM31" (0.000004)
4839. feature "RPL12P25" (0.000004)
4840. feature "PEAR1" (0.000004)
4841. feature "GRAMD4P3" (0.000004)
4842. feature "BBS4" (0.000004)
4843. feature "RGL3" (0.000004)
4844. feature "NXN" (0.000004)
4845. feature "PLAAT4" (0.000004)
4846. feature "FGF22" (0.000004)
4847. feature "ADAM19" (0.000004)
4848. feature "GPRC5D-AS1" (0.000004)
4849. feature "BATF3" (0.000004)
4850. feature "RPS6P26" (0.000004)
4851. feature "CYB561D1" (0.000004)
4852. feature "LINC00672" (0.000004)
4853. feature "LRRC37A" (0.000004)
4854. feature "NIBAN3" (0.000004)
4855. feature "EHD3" (0.000004)
4856. feature "FGFR2" (0.000004)
4857. feature "CYP2J2" (0.000004)
4858. feature "SERPINB9P1" (0.000004)
4859. feature "LRCH4" (0.000004)
4860. feature "DEPP1" (0.000004)
4861. feature "ZNF81" (0.000004)
4862. feature "L3HYPDH" (0.000004)
4863. feature "ARL6" (0.000004)
4864. feature "ALDH1A3" (0.000004)
4865. feature "FN1" (0.000004)
4866. feature "PTPRN" (0.000004)
4867. feature "QPCTL" (0.000004)
4868. feature "FOXO3B" (0.000004)
4869. feature "YWHAEP5" (0.000004)
4870. feature "DLX2" (0.000004)
4871. feature "AGK" (0.000004)
4872. feature "HERC3" (0.000004)
4873. feature "INPP1" (0.000004)
4874. feature "PSD3" (0.000004)
4875. feature "MIR194-2HG" (0.000004)
4876. feature "GLIPR1" (0.000004)
4877. feature "CRY2" (0.000004)
4878. feature "PDLIM5" (0.000004)
4879. feature "LRRIQ1" (0.000004)
4880. feature "YBX1P6" (0.000004)
4881. feature "KRTAP20-4" (0.000004)
4882. feature "ISOC1" (0.000004)
4883. feature "MCM7" (0.000004)
4884. feature "HOXC6" (0.000004)
4885. feature "ACTR1B" (0.000004)
4886. feature "MAP3K2-DT" (0.000004)
4887. feature "TAT-AS1" (0.000004)
4888. feature "TRIM3" (0.000004)
4889. feature "BORA" (0.000003)
4890. feature "LRTM1" (0.000003)
4891. feature "WRAP73" (0.000003)
4892. feature "SCD5" (0.000003)
4893. feature "CFAP45" (0.000003)
4894. feature "DHX35-DT" (0.000003)
4895. feature "REM2" (0.000003)
4896. feature "NIF3L1" (0.000003)
4897. feature "C2CD2L" (0.000003)
4898. feature "RHOXF2" (0.000003)
4899. feature "LINC00511" (0.000003)
4900. feature "GABRE" (0.000003)
4901. feature "NKAIN1" (0.000003)
4902. feature "SLC7A8" (0.000003)
4903. feature "TGFBI" (0.000003)
4904. feature "CUEDC1" (0.000003)
4905. feature "ARRDC1" (0.000003)
4906. feature "ALPP" (0.000003)
4907. feature "MYPN" (0.000003)
4908. feature "NEK8" (0.000003)
4909. feature "RPL21P34" (0.000003)
4910. feature "F3" (0.000003)
4911. feature "PSMD10P2" (0.000003)
4912. feature "DYNC1LI2-DT" (0.000003)
4913. feature "CLSTN3" (0.000003)
4914. feature "ADRA2C" (0.000003)
4915. feature "ISYNA1" (0.000003)
4916. feature "ZNF333" (0.000003)
4917. feature "LINC01186" (0.000003)
4918. feature "B3GALT5" (0.000003)
4919. feature "LINC01293" (0.000003)
4920. feature "KNDC1" (0.000003)
4921. feature "LINC01931" (0.000003)
4922. feature "FAXC" (0.000003)
4923. feature "MAPK11" (0.000003)
4924. feature "MSH5" (0.000003)
4925. feature "NEURL1" (0.000003)
4926. feature "STIMATE" (0.000003)
4927. feature "SYCP3" (0.000003)
4928. feature "UCP2" (0.000003)
4929. feature "PHC1" (0.000003)
4930. feature "ZIC1" (0.000003)
4931. feature "DAB2IP" (0.000003)
4932. feature "IPO8P1" (0.000003)
4933. feature "SPATA2" (0.000003)
4934. feature "GDPD5" (0.000003)
4935. feature "CTNS" (0.000003)
4936. feature "SIRT5" (0.000003)
4937. feature "RPS6KL1" (0.000003)
4938. feature "NPEPL1" (0.000003)
4939. feature "ASIC1" (0.000003)
4940. feature "SLC23A2" (0.000003)
4941. feature "CEACAM5" (0.000003)
4942. feature "R3HCC1" (0.000003)
4943. feature "NAP1L6P" (0.000003)
4944. feature "TACSTD2" (0.000003)
4945. feature "LINC01460" (0.000003)
4946. feature "SEMA4B" (0.000003)
4947. feature "LBX2" (0.000003)
4948. feature "COLCA1" (0.000003)
4949. feature "DEFB1" (0.000003)
4950. feature "TMCC2" (0.000003)
4951. feature "AOX1" (0.000003)
4952. feature "KIZ" (0.000003)
4953. feature "NRG2" (0.000003)
4954. feature "PPARGC1B" (0.000003)
4955. feature "C14orf132" (0.000003)
4956. feature "CXCL3" (0.000003)
4957. feature "DZIP1L" (0.000003)
4958. feature "PDGFB" (0.000003)
4959. feature "ABCA4" (0.000003)
4960. feature "TCF4" (0.000003)
4961. feature "NUP188" (0.000003)
4962. feature "MMD" (0.000003)
4963. feature "E2F8" (0.000003)
4964. feature "MRNIP" (0.000003)
4965. feature "TBXAS1" (0.000003)
4966. feature "TULP2" (0.000003)
4967. feature "ZNF112" (0.000003)
4968. feature "ZNF778" (0.000003)
4969. feature "OSGIN1" (0.000003)
4970. feature "CENPA" (0.000003)
4971. feature "ZBED8" (0.000003)
4972. feature "IER2" (0.000003)
4973. feature "KIF11" (0.000003)
4974. feature "L3MBTL3" (0.000003)
4975. feature "P3H2" (0.000003)
4976. feature "LINC01907" (0.000003)
4977. feature "ZNF674-AS1" (0.000003)
4978. feature "PTPRZ1" (0.000003)
4979. feature "ATP8B3" (0.000003)
4980. feature "KATNB1" (0.000003)
4981. feature "KYNU" (0.000003)
4982. feature "HHLA2" (0.000003)
4983. feature "MAN1C1" (0.000003)
4984. feature "ITPR1-DT" (0.000003)
4985. feature "STAG3" (0.000003)
4986. feature "DTX3" (0.000003)
4987. feature "PLPP2" (0.000003)
4988. feature "DANT2" (0.000003)
4989. feature "GPR156" (0.000003)
4990. feature "CSF2" (0.000003)
4991. feature "LINC02894" (0.000003)
4992. feature "SLC4A2" (0.000003)
4993. feature "TATDN2P2" (0.000003)
4994. feature "RAD52" (0.000003)
4995. feature "ZNF79" (0.000003)
4996. feature "TRIM62" (0.000003)
4997. feature "FHDC1" (0.000003)
4998. feature "HLA-B" (0.000003)
4999. feature "LINC02693" (0.000003)
5000. feature "EPDR1" (0.000003)
5001. feature "LINC00844" (0.000003)
5002. feature "INTS6-AS1" (0.000003)
5003. feature "ZNF343" (0.000003)
5004. feature "CACNA1B" (0.000003)
5005. feature "BLOC1S6P1" (0.000003)
5006. feature "ZNF568" (0.000003)
5007. feature "CLEC3A" (0.000003)
5008. feature "ZNF449" (0.000003)
5009. feature "SERTAD4-AS1" (0.000003)
5010. feature "FAM184A" (0.000003)
5011. feature "CRAT" (0.000003)
5012. feature "ABCA12" (0.000003)
5013. feature "C17orf107" (0.000003)
5014. feature "LINC01772" (0.000003)
5015. feature "BSCL2" (0.000003)
5016. feature "ERO1B" (0.000003)
5017. feature "MYO3B" (0.000003)
5018. feature "CORO7" (0.000003)
5019. feature "ARHGEF35" (0.000003)
5020. feature "GCM1" (0.000003)
5021. feature "LARGE1" (0.000003)
5022. feature "LRFN2" (0.000003)
5023. feature "THAP2" (0.000003)
5024. feature "ABHD8" (0.000003)
5025. feature "ZFYVE1" (0.000003)
5026. feature "CDK18" (0.000003)
5027. feature "BHLHE40-AS1" (0.000003)
5028. feature "DENND3-AS1" (0.000003)
5029. feature "C16orf95-DT" (0.000003)
5030. feature "OSCP1" (0.000003)
5031. feature "CYP3A5" (0.000003)
5032. feature "CTSO" (0.000003)
5033. feature "STAT6" (0.000003)
5034. feature "ELFN1" (0.000003)
5035. feature "TLE1" (0.000003)
5036. feature "GBP2" (0.000003)
5037. feature "TOGARAM2" (0.000003)
5038. feature "LINC00352" (0.000003)
5039. feature "RARB" (0.000003)
5040. feature "C1orf167" (0.000003)
5041. feature "PLA2R1" (0.000003)
5042. feature "ANKFN1" (0.000003)
5043. feature "ST6GALNAC1" (0.000003)
5044. feature "SIAH1" (0.000003)
5045. feature "RGCC" (0.000003)
5046. feature "TRAF1" (0.000003)
5047. feature "ATRNL1" (0.000003)
5048. feature "FST" (0.000003)
5049. feature "ADGRG2" (0.000003)
5050. feature "ACTR5" (0.000003)
5051. feature "SYT16" (0.000003)
5052. feature "PLOD1" (0.000003)
5053. feature "PLIN5" (0.000003)
5054. feature "PPP1R36" (0.000003)
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8462. feature "WDR4" (0.000000)
8463. feature "KCNN2" (0.000000)
8464. feature "CSMD3" (0.000000)
8465. feature "RNF224" (0.000000)
8466. feature "BCL2L11" (0.000000)
8467. feature "ZNF551" (0.000000)
8468. feature "USP51" (0.000000)
8469. feature "COL6A2" (0.000000)
8470. feature "ABCC1" (0.000000)
8471. feature "ERBB3" (0.000000)
8472. feature "VPS13C-DT" (0.000000)
8473. feature "MMRN2" (0.000000)
8474. feature "IRX3" (0.000000)
8475. feature "ACAT2" (0.000000)
8476. feature "TLE6" (0.000000)
8477. feature "MXD4" (0.000000)
8478. feature "LINC01237" (0.000000)
8479. feature "ZNF587P1" (0.000000)
8480. feature "FERMT1" (0.000000)
8481. feature "TNFAIP8L3" (0.000000)
8482. feature "C1orf216" (0.000000)
8483. feature "TMPPE" (0.000000)
8484. feature "RNA5SP155" (0.000000)
8485. feature "HSPA7" (0.000000)
8486. feature "TENT5A" (0.000000)
8487. feature "LDB3" (0.000000)
8488. feature "KCNK12" (0.000000)
8489. feature "POU5F1B" (0.000000)
8490. feature "SLC12A1" (0.000000)
8491. feature "HSPA1L" (0.000000)
8492. feature "THBS1-IT1" (0.000000)
8493. feature "LYG1" (0.000000)
8494. feature "SMPDL3B" (0.000000)
8495. feature "LHX4" (0.000000)
8496. feature "PLBD1" (0.000000)
8497. feature "NT5CP1" (0.000000)
8498. feature "PFKFB1" (0.000000)
8499. feature "IFT172" (0.000000)
8500. feature "ADHFE1" (0.000000)
8501. feature "SEMA4F" (0.000000)
8502. feature "NECTIN3-AS1" (0.000000)
8503. feature "THOP1" (0.000000)
8504. feature "PGAM1P7" (0.000000)
8505. feature "PGR" (0.000000)
8506. feature "ARMH1" (0.000000)
8507. feature "EEF1GP4" (0.000000)
8508. feature "FAM227A" (0.000000)
8509. feature "ZNF792" (0.000000)
8510. feature "LINC02716" (0.000000)
8511. feature "LINC00862" (0.000000)
8512. feature "MCC" (0.000000)
8513. feature "RASL11B" (0.000000)
8514. feature "MCAM" (0.000000)
8515. feature "PGGHG" (0.000000)
8516. feature "PRPF19-DT" (0.000000)
8517. feature "LINC02613" (0.000000)
8518. feature "SLC4A3" (0.000000)
8519. feature "OR8G3P" (0.000000)
8520. feature "SNED1" (0.000000)
8521. feature "AKR7A3" (0.000000)
8522. feature "H1-6" (0.000000)
8523. feature "TMEM37" (0.000000)
8524. feature "SLC27A2" (0.000000)
8525. feature "PDE4B" (0.000000)
8526. feature "ANPEP" (0.000000)
8527. feature "SERPINB3" (0.000000)
8528. feature "ADIRF-AS1" (0.000000)
8529. feature "HHIPL1" (0.000000)
8530. feature "GNE" (0.000000)
8531. feature "ITGAM" (0.000000)
8532. feature "SLC6A2" (0.000000)
8533. feature "C3" (0.000000)
8534. feature "CDYL2" (0.000000)
8535. feature "DHX58" (0.000000)
8536. feature "AEBP1" (0.000000)
8537. feature "ARL11" (0.000000)
8538. feature "ZNF229" (0.000000)
8539. feature "ENPP2" (0.000000)
8540. feature "CLDN10" (0.000000)
8541. feature "CHST2" (0.000000)
8542. feature "TSPAN7" (0.000000)
8543. feature "CYP4F11" (0.000000)
8544. feature "TNFRSF8" (0.000000)
8545. feature "WTAPP1" (0.000000)
8546. feature "IL22RA1" (0.000000)
8547. feature "CYP4F12" (0.000000)
8548. feature "TNFSF18" (0.000000)
8549. feature "MYO7B" (0.000000)
8550. feature "LCK" (0.000000)
8551. feature "TCN1" (0.000000)
8552. feature "FABP4" (0.000000)
8553. feature "ESX1" (0.000000)
8554. feature "GJB4" (0.000000)
8555. feature "TEKT3" (0.000000)
8556. feature "SV2A" (0.000000)
8557. feature "PROM1" (0.000000)
8558. feature "IDO1" (0.000000)
8559. feature "CERS3" (0.000000)
8560. feature "ENPP1" (0.000000)
8561. feature "TENM2" (0.000000)
8562. feature "SCG2" (0.000000)
8563. feature "PTPRO" (0.000000)
8564. feature "FAM180A" (0.000000)
8565. feature "NEO1" (0.000000)
8566. feature "ERVFRD-1" (0.000000)
8567. feature "SLC24A1" (0.000000)
8568. feature "LINC01537" (0.000000)
8569. feature "GFPT2" (0.000000)
8570. feature "ZNF844" (0.000000)
8571. feature "FGF19" (0.000000)
8572. feature "SERPINB13" (0.000000)
8573. feature "SNPH" (0.000000)
8574. feature "CCBE1" (0.000000)
8575. feature "F2RL2" (0.000000)
8576. feature "UBA7" (0.000000)
8577. feature "SUSD4" (0.000000)
8578. feature "MATN1-AS1" (0.000000)
8579. feature "LINC01018" (0.000000)
8580. feature "RNF128" (0.000000)
8581. feature "CDH23" (0.000000)
8582. feature "FBLN5" (0.000000)
8583. feature "STK32B" (0.000000)
8584. feature "MUC20" (0.000000)
8585. feature "LVRN" (0.000000)
8586. feature "GJB6" (0.000000)
8587. feature "TRBC2" (0.000000)
8588. feature "PIP5K1B" (0.000000)
8589. feature "AKNA" (0.000000)
8590. feature "EEF1DP3" (0.000000)
8591. feature "MN1" (0.000000)
8592. feature "CELF6" (0.000000)
8593. feature "CCL27" (0.000000)
8594. feature "TSPYL2" (0.000000)
8595. feature "EPHB1" (0.000000)
8596. feature "TMEM163" (0.000000)
8597. feature "LMOD1" (0.000000)
8598. feature "RTL5" (0.000000)
8599. feature "TESC" (0.000000)
8600. feature "AMTN" (0.000000)
8601. feature "TRIM34" (0.000000)
8602. feature "CLDN23" (0.000000)
8603. feature "SMOC2" (0.000000)
8604. feature "TEDC1" (0.000000)
8605. feature "PLA2G4C" (0.000000)
8606. feature "NUDT11" (0.000000)
8607. feature "HRH3" (0.000000)
8608. feature "ADAMTS17" (0.000000)
8609. feature "ANKRD22" (0.000000)
8610. feature "ZMYND12" (0.000000)
8611. feature "ARL4C" (0.000000)
8612. feature "IARS1" (0.000000)
8613. feature "LY6K" (0.000000)
8614. feature "ABLIM3" (0.000000)
8615. feature "TMEM204" (0.000000)
8616. feature "WNT3" (0.000000)
8617. feature "PIANP" (0.000000)
8618. feature "GJB2" (0.000000)
8619. feature "THY1" (0.000000)
8620. feature "CLCA2" (0.000000)
8621. feature "SERPINB10" (0.000000)
8622. feature "CYP4F3" (0.000000)
8623. feature "HEPHL1" (0.000000)
8624. feature "SCEL" (0.000000)
8625. feature "SDHA" (0.000000)
8626. feature "ARSI" (0.000000)
8627. feature "MMP10" (0.000000)
8628. feature "MEST" (0.000000)
8629. feature "VIM" (0.000000)
8630. feature "CLDN8" (0.000000)
8631. feature "VILL" (0.000000)
8632. feature "CD27-AS1" (0.000000)
8633. feature "CCNB3" (0.000000)
8634. feature "SERPINB2" (0.000000)
8635. feature "ALDH1A1" (0.000000)
8636. feature "MAGEC2" (0.000000)
8637. feature "DHRS9" (0.000000)
8638. feature "LRRC38" (0.000000)
8639. feature "FAM13A-AS1" (0.000000)
8640. feature "CDK5R1" (0.000000)
8641. feature "DENND5A" (0.000000)
8642. feature "SLC25A27" (0.000000)
8643. feature "GRIK5" (0.000000)
8644. feature "DNAI7" (0.000000)
8645. feature "FAAHP1" (0.000000)

¶

4.6.3) XGBoost

Also in this case we built the Xgboost classifier.

We exploited its property of running faster to increase the number of hyperparameters for the estimators.

The results were good as this general model achieves a performance of 96% on the test set, that we consider as outstanding.

In [ ]:
#i build the XGBoost classifier for the general dataset
from xgboost import XGBClassifier

# Define the XGBoost classifier
xgb = XGBClassifier(random_state=42)

# Define the parameter grid for GridSearchCV
param_grid = {'n_estimators': [50, 100], 'max_depth': [30, 50]}
grid_search = GridSearchCV(xgb, param_grid, cv=5)

outer_cv = KFold(n_splits=2, shuffle=True, random_state=42)

# Perform the cross validation
scores = cross_val_score(grid_search, X_train_all, y_train_all, cv=outer_cv)

# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))

# Perform GridSearchCV on the entire dataset to get the best parameters and fit the model
grid_search.fit(X_train_all, y_train_all)
print("Best hyperparameters: ", grid_search.best_params_)
print("Best accuracy: ", grid_search.best_score_)
print("Best model: ", grid_search.best_estimator_)

#i want to test the model on the test dataset
print("Test accuracy: ", grid_search.best_estimator_.score(X_test_all, y_test_all))

# Print the feature importances
importances = grid_search.best_estimator_.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
for f in range(X_train_all.shape[1]):
    print("%d. feature %s (%f)" % (f + 1, X_train_all.columns[indices[f]], importances[indices[f]]))

best_xgb_all = grid_search.best_estimator_

Mean accuracy of nested cross validation:  0.9561007854421029
Best hyperparameters:  {'max_depth': 50, 'n_estimators': 100}
Best accuracy:  0.95975575976527
Best model:  XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=50, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=100, n_jobs=None,
              num_parallel_tree=None, random_state=42, ...)
Test accuracy:  0.9608056614044638
Feature ranking:
1. feature "TMSB10" (0.036074)
2. feature "NDRG1" (0.030002)
3. feature "PGK1" (0.014176)
4. feature "IGFBP3" (0.008976)
5. feature "KRT19" (0.007397)
6. feature "GAPDH" (0.007392)
7. feature "BCYRN1" (0.006153)
8. feature "CCDC88A" (0.005787)
9. feature "TFF1" (0.005495)
10. feature "RPL41" (0.005456)
11. feature "LGALS1" (0.005203)
12. feature "MT-RNR2" (0.005140)
13. feature "BNIP3" (0.004985)
14. feature "CHAF1A" (0.004796)
15. feature "AATF" (0.004676)
16. feature "MT-TP" (0.004099)
17. feature "GNAQ" (0.004074)
18. feature "MCM6" (0.003944)
19. feature "JAK1" (0.003754)
20. feature "P4HA1" (0.003651)
21. feature "SLC6A8" (0.003547)
22. feature "LIMCH1" (0.003414)
23. feature "COMMD6" (0.003262)
24. feature "AIP" (0.003238)
25. feature "AKR1C1" (0.003200)
26. feature "NAA50" (0.003135)
27. feature "FNIP1" (0.003102)
28. feature "PRPSAP2" (0.003101)
29. feature "PHRF1" (0.003056)
30. feature "NIBAN2" (0.002948)
31. feature "EGLN3" (0.002941)
32. feature "LDHA" (0.002843)
33. feature "MT-RNR1" (0.002842)
34. feature "LINC02367" (0.002840)
35. feature "CA9" (0.002812)
36. feature "OTULIN" (0.002681)
37. feature "HNRNPA1" (0.002666)
38. feature "DKK1" (0.002664)
39. feature "KMT5B" (0.002660)
40. feature "ZNF33B" (0.002640)
41. feature "MT-ND3" (0.002621)
42. feature "ZFC3H1" (0.002589)
43. feature "MAD2L2" (0.002586)
44. feature "LDHB" (0.002580)
45. feature "STARD7" (0.002563)
46. feature "PUSL1" (0.002495)
47. feature "BNIP3L" (0.002435)
48. feature "TOB1" (0.002383)
49. feature "PCDHGA10" (0.002370)
50. feature "RAD21" (0.002351)
51. feature "MT1E" (0.002305)
52. feature "CTBP2" (0.002299)
53. feature "EBAG9" (0.002281)
54. feature "TNIP2" (0.002270)
55. feature "SOCS4" (0.002261)
56. feature "PCBD1" (0.002252)
57. feature "ENO1" (0.002226)
58. feature "LSM14A" (0.002218)
59. feature "MT-CYB" (0.002207)
60. feature "CIRBP" (0.002199)
61. feature "ZNF121" (0.002196)
62. feature "FAM111B" (0.002195)
63. feature "TMEM64" (0.002191)
64. feature "BRMS1" (0.002155)
65. feature "C8orf58" (0.002142)
66. feature "GNB1" (0.002113)
67. feature "RPS26" (0.002099)
68. feature "PRRC2A" (0.002074)
69. feature "TASOR" (0.002068)
70. feature "AKR1C2" (0.002057)
71. feature "PTOV1" (0.002043)
72. feature "SDHB" (0.002037)
73. feature "CDK2AP2" (0.002037)
74. feature "CD47" (0.002034)
75. feature "NUMA1" (0.002030)
76. feature "CENPB" (0.002019)
77. feature "PRR11" (0.002005)
78. feature "MCL1" (0.002002)
79. feature "UBE2Q1" (0.001985)
80. feature "MLLT3" (0.001973)
81. feature "BMPR1B" (0.001968)
82. feature "UGCG" (0.001960)
83. feature "VIT" (0.001957)
84. feature "NR2F6" (0.001947)
85. feature "KLC2" (0.001939)
86. feature "CADM1" (0.001939)
87. feature "PLOD2" (0.001908)
88. feature "MRPL55" (0.001906)
89. feature "LTBR" (0.001873)
90. feature "L3MBTL2" (0.001868)
91. feature "S100A10" (0.001850)
92. feature "EFNA2" (0.001847)
93. feature "PHACTR1" (0.001846)
94. feature "MAPK14" (0.001844)
95. feature "GOLGA3" (0.001802)
96. feature "SET" (0.001782)
97. feature "RAB12" (0.001779)
98. feature "B4GALT1" (0.001775)
99. feature "RPLP0P2" (0.001758)
100. feature "TPI1" (0.001750)
101. feature "SRM" (0.001727)
102. feature "RBBP8" (0.001705)
103. feature "MARK2" (0.001671)
104. feature "KPNA4" (0.001669)
105. feature "ISOC2" (0.001619)
106. feature "ACTB" (0.001619)
107. feature "PDXK" (0.001613)
108. feature "EIF3J" (0.001609)
109. feature "RPS5" (0.001604)
110. feature "MT-ND4L" (0.001596)
111. feature "MDFI" (0.001560)
112. feature "SENP7" (0.001559)
113. feature "C2CD2L" (0.001554)
114. feature "PLAAT3" (0.001551)
115. feature "DEDD" (0.001543)
116. feature "H3C2" (0.001538)
117. feature "DYNLL2" (0.001535)
118. feature "GDF15" (0.001507)
119. feature "H3-3A" (0.001500)
120. feature "SELENOH" (0.001496)
121. feature "YKT6" (0.001496)
122. feature "PGAP6" (0.001491)
123. feature "MSANTD3" (0.001487)
124. feature "MT-ATP6" (0.001483)
125. feature "PCYT1B" (0.001478)
126. feature "LRRC59" (0.001447)
127. feature "C6orf132" (0.001445)
128. feature "UBE2E1" (0.001437)
129. feature "H2AZ2" (0.001436)
130. feature "SNRNP70" (0.001436)
131. feature "PPFIA1" (0.001435)
132. feature "POLR2I" (0.001432)
133. feature "PAPSS2" (0.001431)
134. feature "FKBP5" (0.001430)
135. feature "BACH1" (0.001426)
136. feature "PKN1" (0.001416)
137. feature "MT-ND4" (0.001412)
138. feature "ATXN2L" (0.001410)
139. feature "ADARB1" (0.001407)
140. feature "KIFC3" (0.001397)
141. feature "VAT1" (0.001392)
142. feature "MT-ND1" (0.001388)
143. feature "DDIT4" (0.001384)
144. feature "MARVELD1" (0.001383)
145. feature "WDR43" (0.001383)
146. feature "HSP90AA1" (0.001371)
147. feature "ANKIB1" (0.001369)
148. feature "MT-ND6" (0.001367)
149. feature "HSPA2" (0.001364)
150. feature "DDX28" (0.001364)
151. feature "EPC2" (0.001364)
152. feature "PKM" (0.001363)
153. feature "XPNPEP1" (0.001359)
154. feature "GIPC1" (0.001352)
155. feature "SUPT5H" (0.001349)
156. feature "RPS19" (0.001349)
157. feature "KRT17" (0.001348)
158. feature "UPF3B" (0.001345)
159. feature "KYNU" (0.001344)
160. feature "MAD2L1" (0.001343)
161. feature "MAP3K13" (0.001339)
162. feature "NMT2" (0.001338)
163. feature "ISCU" (0.001337)
164. feature "STARD10" (0.001335)
165. feature "SLC1A5" (0.001333)
166. feature "ZNF688" (0.001331)
167. feature "GYS1" (0.001325)
168. feature "DCAF7" (0.001319)
169. feature "NEDD1" (0.001317)
170. feature "HSPD1" (0.001316)
171. feature "ARL13B" (0.001314)
172. feature "GDI1" (0.001309)
173. feature "FGF23" (0.001305)
174. feature "TPM1" (0.001298)
175. feature "SHKBP1" (0.001297)
176. feature "MALAT1" (0.001297)
177. feature "USP7" (0.001286)
178. feature "HES1" (0.001285)
179. feature "C7orf50" (0.001284)
180. feature "CDC42BPA" (0.001284)
181. feature "ZFAS1" (0.001282)
182. feature "GPM6A" (0.001282)
183. feature "RPL34" (0.001280)
184. feature "RALBP1" (0.001279)
185. feature "DGUOK-AS1" (0.001278)
186. feature "NDUFB4" (0.001277)
187. feature "PUS7" (0.001272)
188. feature "TTLL5" (0.001271)
189. feature "RHNO1" (0.001266)
190. feature "PTPN11" (0.001256)
191. feature "SH3RF1" (0.001256)
192. feature "MT-TV" (0.001253)
193. feature "TCF20" (0.001246)
194. feature "PCNX2" (0.001245)
195. feature "MRPS33" (0.001244)
196. feature "GNAS" (0.001244)
197. feature "LINC01551" (0.001243)
198. feature "PLSCR1" (0.001240)
199. feature "NRIP1" (0.001240)
200. feature "IRX2" (0.001238)
201. feature "MTND1P23" (0.001237)
202. feature "BRD7" (0.001237)
203. feature "CHD8" (0.001235)
204. feature "ZNF316" (0.001232)
205. feature "AURKA" (0.001231)
206. feature "RPL21" (0.001225)
207. feature "EGR1" (0.001222)
208. feature "HK1" (0.001222)
209. feature "RPS14" (0.001221)
210. feature "MGRN1" (0.001221)
211. feature "TMSB4XP4" (0.001220)
212. feature "OBSCN" (0.001215)
213. feature "RPL39" (0.001214)
214. feature "MDM4" (0.001213)
215. feature "CEP104" (0.001211)
216. feature "AVPI1" (0.001208)
217. feature "TPT1" (0.001206)
218. feature "MT-TS1" (0.001206)
219. feature "ODF2" (0.001203)
220. feature "RPS21" (0.001200)
221. feature "SYT14" (0.001200)
222. feature "C4orf3" (0.001199)
223. feature "ARIH1" (0.001198)
224. feature "POLR2A" (0.001197)
225. feature "EPS8L2" (0.001182)
226. feature "ENAH" (0.001175)
227. feature "STRBP" (0.001173)
228. feature "CARMIL1" (0.001168)
229. feature "RRS1" (0.001164)
230. feature "C12orf75" (0.001157)
231. feature "NORAD" (0.001152)
232. feature "BATF" (0.001150)
233. feature "ANAPC16" (0.001150)
234. feature "FLNA" (0.001143)
235. feature "CAP1" (0.001142)
236. feature "MIR663AHG" (0.001141)
237. feature "LRRFIP2" (0.001139)
238. feature "AMFR" (0.001137)
239. feature "GADD45GIP1" (0.001136)
240. feature "RPL30" (0.001135)
241. feature "NSMF" (0.001134)
242. feature "G6PD" (0.001132)
243. feature "CFAP36" (0.001126)
244. feature "MNS1" (0.001119)
245. feature "RPL10" (0.001116)
246. feature "IGFL2-AS1" (0.001113)
247. feature "AXL" (0.001112)
248. feature "RPL18A" (0.001111)
249. feature "KIAA1522" (0.001111)
250. feature "GSPT1" (0.001105)
251. feature "SAMD15" (0.001102)
252. feature "PFDN4" (0.001102)
253. feature "UBA52" (0.001099)
254. feature "CEP83" (0.001092)
255. feature "MRFAP1" (0.001091)
256. feature "GDPGP1" (0.001089)
257. feature "KMT2D" (0.001089)
258. feature "GARS1" (0.001079)
259. feature "SSX2IP" (0.001078)
260. feature "SLC25A25-AS1" (0.001076)
261. feature "SMG7" (0.001074)
262. feature "MT-CO3" (0.001068)
263. feature "HMGN3" (0.001067)
264. feature "RHOA" (0.001065)
265. feature "PHF12" (0.001062)
266. feature "ZFP36L1" (0.001060)
267. feature "YTHDC1" (0.001057)
268. feature "KAT7" (0.001057)
269. feature "C20orf27" (0.001057)
270. feature "UTP18" (0.001057)
271. feature "CBX4" (0.001053)
272. feature "ZCCHC9" (0.001053)
273. feature "TNKS1BP1" (0.001051)
274. feature "KCNJ3" (0.001048)
275. feature "SVIP" (0.001044)
276. feature "FAM222B" (0.001041)
277. feature "TSPYL1" (0.001040)
278. feature "EIF5AL1" (0.001040)
279. feature "ELP2" (0.001032)
280. feature "PSAP" (0.001031)
281. feature "C19orf53" (0.001030)
282. feature "KIF16B" (0.001021)
283. feature "NFE2L2" (0.001019)
284. feature "MYBL1" (0.001018)
285. feature "SPACA6" (0.001017)
286. feature "COX14" (0.001016)
287. feature "PFN1" (0.001014)
288. feature "ROMO1" (0.001012)
289. feature "ZNF629" (0.001007)
290. feature "MT-CO2" (0.001007)
291. feature "TASOR2" (0.001006)
292. feature "ESRP2" (0.001005)
293. feature "NEAT1" (0.001004)
294. feature "RPS6KA6" (0.001001)
295. feature "GOLGA5" (0.001000)
296. feature "MYL12B" (0.001000)
297. feature "RPSAP48" (0.000999)
298. feature "FOXP4" (0.000997)
299. feature "ODF2L" (0.000991)
300. feature "NPM1P40" (0.000986)
301. feature "FOSL1" (0.000986)
302. feature "SOX4" (0.000978)
303. feature "LMNB2" (0.000977)
304. feature "PGAM1" (0.000974)
305. feature "KIF3B" (0.000973)
306. feature "CETN3" (0.000972)
307. feature "SUMO3" (0.000970)
308. feature "GTF2F1" (0.000968)
309. feature "C6orf62" (0.000957)
310. feature "PIP4K2A" (0.000956)
311. feature "MT-CO1" (0.000956)
312. feature "CENPE" (0.000955)
313. feature "SNRPD2" (0.000951)
314. feature "AHNAK2" (0.000950)
315. feature "RHOD" (0.000948)
316. feature "WSB2" (0.000947)
317. feature "MT-ND2" (0.000946)
318. feature "MRPL14" (0.000946)
319. feature "TMPRSS4" (0.000946)
320. feature "NDUFB10" (0.000945)
321. feature "AP1G1" (0.000941)
322. feature "THRB" (0.000938)
323. feature "MAVS" (0.000937)
324. feature "DNAJA1" (0.000937)
325. feature "CHP1" (0.000935)
326. feature "UBQLN4" (0.000933)
327. feature "H2AC12" (0.000929)
328. feature "PACS2" (0.000928)
329. feature "RPL15" (0.000927)
330. feature "UCA1" (0.000926)
331. feature "ANKRD52" (0.000924)
332. feature "ACTG1" (0.000923)
333. feature "CANT1" (0.000922)
334. feature "SMARCD2" (0.000919)
335. feature "MT-TQ" (0.000914)
336. feature "SLC25A37" (0.000913)
337. feature "IGFBP4" (0.000910)
338. feature "H2AC20" (0.000910)
339. feature "DDX54" (0.000908)
340. feature "CCDC34" (0.000908)
341. feature "MCM5" (0.000907)
342. feature "CACNA1A" (0.000907)
343. feature "PHGDH" (0.000904)
344. feature "NCL" (0.000901)
345. feature "DNAJA2" (0.000901)
346. feature "KRT7" (0.000900)
347. feature "CITED2" (0.000897)
348. feature "KRT5" (0.000896)
349. feature "MT1X" (0.000895)
350. feature "MARCKS" (0.000895)
351. feature "SRC" (0.000894)
352. feature "CALR" (0.000893)
353. feature "ATP5MG" (0.000893)
354. feature "CDK5RAP2" (0.000891)
355. feature "CSRP1" (0.000891)
356. feature "RPL13" (0.000889)
357. feature "PPP1R14B" (0.000888)
358. feature "TSR1" (0.000887)
359. feature "RPS15A" (0.000883)
360. feature "MAP4K4" (0.000883)
361. feature "RRM1" (0.000880)
362. feature "SECISBP2" (0.000879)
363. feature "AMOTL1" (0.000879)
364. feature "PLBD2" (0.000879)
365. feature "CTNNA1-AS1" (0.000879)
366. feature "TFF3" (0.000874)
367. feature "INPP4B" (0.000873)
368. feature "PRPF40A" (0.000869)
369. feature "RPL23" (0.000869)
370. feature "SYTL2" (0.000867)
371. feature "PSIP1" (0.000864)
372. feature "CAMSAP2" (0.000862)
373. feature "C16orf91" (0.000861)
374. feature "TK1" (0.000860)
375. feature "RNF20" (0.000855)
376. feature "H2BC9" (0.000853)
377. feature "TRIM41" (0.000851)
378. feature "SSNA1" (0.000849)
379. feature "S100A6" (0.000849)
380. feature "PROSER1" (0.000849)
381. feature "LPP" (0.000843)
382. feature "EPN3" (0.000840)
383. feature "TAOK1" (0.000838)
384. feature "RAB11B" (0.000837)
385. feature "KRT10" (0.000837)
386. feature "PFKP" (0.000835)
387. feature "RNF122" (0.000835)
388. feature "MTA2" (0.000831)
389. feature "PKIB" (0.000830)
390. feature "TAF13" (0.000830)
391. feature "HMGN5" (0.000829)
392. feature "RALGPS1" (0.000829)
393. feature "NETO2" (0.000828)
394. feature "CALM2" (0.000826)
395. feature "RPS19BP1" (0.000826)
396. feature "GTF3A" (0.000824)
397. feature "HIF1A" (0.000823)
398. feature "CDV3" (0.000823)
399. feature "UQCR11" (0.000823)
400. feature "RPS28" (0.000823)
401. feature "RPS3" (0.000822)
402. feature "METAP2" (0.000818)
403. feature "RPL12" (0.000817)
404. feature "RPLP1" (0.000817)
405. feature "RAB13" (0.000816)
406. feature "SCP2" (0.000816)
407. feature "FASTK" (0.000815)
408. feature "LAD1" (0.000811)
409. feature "MBD2" (0.000810)
410. feature "GBP1P1" (0.000809)
411. feature "ACKR3" (0.000804)
412. feature "TIAL1" (0.000804)
413. feature "FGF12" (0.000803)
414. feature "TPP1" (0.000802)
415. feature "PI4KB" (0.000800)
416. feature "HCFC1R1" (0.000800)
417. feature "DONSON" (0.000798)
418. feature "EPB41L1" (0.000798)
419. feature "ZC3H15" (0.000796)
420. feature "TMA7" (0.000796)
421. feature "MARK3" (0.000792)
422. feature "DLD" (0.000789)
423. feature "RHOBTB3" (0.000786)
424. feature "KEAP1" (0.000786)
425. feature "FTL" (0.000785)
426. feature "LAMB3" (0.000783)
427. feature "ARID4B" (0.000781)
428. feature "SLC9A3R1" (0.000780)
429. feature "ATP5F1E" (0.000778)
430. feature "DPM3" (0.000776)
431. feature "RAD23A" (0.000776)
432. feature "MGLL" (0.000773)
433. feature "SOS2" (0.000771)
434. feature "NCALD" (0.000770)
435. feature "LEO1" (0.000769)
436. feature "ASB8" (0.000765)
437. feature "PPDPF" (0.000763)
438. feature "ARSA" (0.000758)
439. feature "PM20D2" (0.000758)
440. feature "ERBIN" (0.000758)
441. feature "CMPK1" (0.000757)
442. feature "MLXIP" (0.000755)
443. feature "PML" (0.000752)
444. feature "PTMS" (0.000750)
445. feature "CLTB" (0.000748)
446. feature "UNC13D" (0.000747)
447. feature "S100A11" (0.000744)
448. feature "PPP2R1A" (0.000743)
449. feature "PDCD4" (0.000742)
450. feature "PPP4R2" (0.000742)
451. feature "NDUFB1" (0.000741)
452. feature "WNK1" (0.000741)
453. feature "REST" (0.000740)
454. feature "STRN4" (0.000739)
455. feature "CSTA" (0.000737)
456. feature "SERF2" (0.000735)
457. feature "PREB" (0.000732)
458. feature "PRPF19" (0.000732)
459. feature "ATAD2" (0.000730)
460. feature "FAM107B" (0.000728)
461. feature "PLEKHH3" (0.000728)
462. feature "RPS8" (0.000726)
463. feature "NBN" (0.000724)
464. feature "TPD52L1" (0.000723)
465. feature "RPS27" (0.000721)
466. feature "PPP2R5D" (0.000719)
467. feature "RPSA" (0.000713)
468. feature "ZCRB1" (0.000711)
469. feature "XRRA1" (0.000710)
470. feature "C4orf48" (0.000709)
471. feature "DHX37" (0.000708)
472. feature "C1orf21" (0.000707)
473. feature "PRRG3" (0.000707)
474. feature "RPL7A" (0.000706)
475. feature "SDC1" (0.000705)
476. feature "FSBP" (0.000705)
477. feature "TCEAL3" (0.000704)
478. feature "KIF5B" (0.000704)
479. feature "RB1CC1" (0.000697)
480. feature "EEF2" (0.000696)
481. feature "UBC" (0.000695)
482. feature "VTI1B" (0.000693)
483. feature "EFHD1" (0.000692)
484. feature "HCFC1" (0.000692)
485. feature "SLPI" (0.000691)
486. feature "HSPA4" (0.000690)
487. feature "PARD6B" (0.000689)
488. feature "VMP1" (0.000689)
489. feature "ANXA1" (0.000688)
490. feature "MT-ATP8" (0.000687)
491. feature "SFN" (0.000687)
492. feature "COTL1" (0.000686)
493. feature "ZNF696" (0.000686)
494. feature "RPS2" (0.000683)
495. feature "ZHX1" (0.000683)
496. feature "COX7C" (0.000680)
497. feature "HSBP1" (0.000679)
498. feature "YWHAZ" (0.000679)
499. feature "ZBTB37" (0.000676)
500. feature "GSTP1" (0.000675)
501. feature "CDIPT" (0.000674)
502. feature "ILF3" (0.000674)
503. feature "H2AC11" (0.000674)
504. feature "RNF168" (0.000672)
505. feature "POLR2J" (0.000671)
506. feature "RPL35" (0.000670)
507. feature "DSCAM-AS1" (0.000667)
508. feature "RPL27A" (0.000667)
509. feature "KRT13" (0.000663)
510. feature "PHF3" (0.000663)
511. feature "PGLS" (0.000661)
512. feature "LSM4" (0.000661)
513. feature "ING2" (0.000659)
514. feature "RTF1" (0.000659)
515. feature "COX7A2" (0.000659)
516. feature "C2orf49" (0.000658)
517. feature "ATP5MK" (0.000658)
518. feature "RPS15" (0.000657)
519. feature "BASP1" (0.000657)
520. feature "RBBP6" (0.000656)
521. feature "CHTOP" (0.000655)
522. feature "MT2A" (0.000652)
523. feature "SMDT1" (0.000652)
524. feature "CTNND1" (0.000651)
525. feature "SLTM" (0.000651)
526. feature "POLE4" (0.000649)
527. feature "COPS9" (0.000648)
528. feature "H4C3" (0.000647)
529. feature "SMC6" (0.000647)
530. feature "OAZ1" (0.000645)
531. feature "POLDIP2" (0.000645)
532. feature "METTL26" (0.000644)
533. feature "BTBD9" (0.000643)
534. feature "BOD1L1" (0.000643)
535. feature "TTLL12" (0.000643)
536. feature "FOXP1" (0.000642)
537. feature "ZNF217" (0.000638)
538. feature "MRPS6" (0.000638)
539. feature "RFC1" (0.000637)
540. feature "NUP62" (0.000635)
541. feature "CEP55" (0.000635)
542. feature "C5orf63" (0.000634)
543. feature "FMN1" (0.000634)
544. feature "HSP90B1" (0.000634)
545. feature "TMEM258" (0.000633)
546. feature "TOP2A" (0.000631)
547. feature "THEM4" (0.000630)
548. feature "SIGMAR1" (0.000629)
549. feature "ZNF302" (0.000629)
550. feature "PSMA7" (0.000628)
551. feature "TSPO" (0.000628)
552. feature "RAC1" (0.000628)
553. feature "FOSL2" (0.000625)
554. feature "EMP2" (0.000622)
555. feature "DIO2" (0.000621)
556. feature "RPL8" (0.000621)
557. feature "PRRC2C" (0.000620)
558. feature "RPS6KB1" (0.000618)
559. feature "AHNAK" (0.000618)
560. feature "HSP90AB1" (0.000618)
561. feature "GATA3" (0.000617)
562. feature "PC" (0.000614)
563. feature "MT-ND5" (0.000614)
564. feature "CAST" (0.000611)
565. feature "LINC01304" (0.000607)
566. feature "GMFB" (0.000606)
567. feature "TPM4" (0.000605)
568. feature "NEDD4L" (0.000605)
569. feature "RGPD4-AS1" (0.000604)
570. feature "MGP" (0.000603)
571. feature "ANKRD11" (0.000603)
572. feature "PLEC" (0.000600)
573. feature "LUZP1" (0.000600)
574. feature "UQCC2" (0.000598)
575. feature "PCF11" (0.000596)
576. feature "KCNQ1OT1" (0.000595)
577. feature "TMSB4X" (0.000595)
578. feature "TRIP12" (0.000594)
579. feature "ZNF148" (0.000593)
580. feature "NCOA3" (0.000592)
581. feature "CAV1" (0.000592)
582. feature "ELP3" (0.000592)
583. feature "ATRX" (0.000592)
584. feature "EIF4EBP2" (0.000591)
585. feature "TPM3" (0.000591)
586. feature "H2BC4" (0.000591)
587. feature "PKP1" (0.000590)
588. feature "DSP" (0.000589)
589. feature "HNRNPD" (0.000588)
590. feature "RBM25" (0.000586)
591. feature "YBX1" (0.000585)
592. feature "DGKZ" (0.000581)
593. feature "PFKFB3" (0.000580)
594. feature "KRT14" (0.000579)
595. feature "NIN" (0.000578)
596. feature "SCYL3" (0.000578)
597. feature "PCSK5" (0.000578)
598. feature "RBM23" (0.000577)
599. feature "TRIM44" (0.000576)
600. feature "RPL37A" (0.000576)
601. feature "GOLGA4" (0.000576)
602. feature "STMN1" (0.000574)
603. feature "NCOR1" (0.000570)
604. feature "RHOV" (0.000570)
605. feature "RPL36" (0.000570)
606. feature "ZNF609" (0.000569)
607. feature "PKP4" (0.000569)
608. feature "H1-4" (0.000565)
609. feature "RPL35A" (0.000564)
610. feature "TRIM37" (0.000563)
611. feature "SAMD4B" (0.000562)
612. feature "ERO1A" (0.000562)
613. feature "KLHL7" (0.000560)
614. feature "R3HDM2" (0.000558)
615. feature "RBFOX2" (0.000558)
616. feature "TRAK1" (0.000557)
617. feature "ZC3H7A" (0.000555)
618. feature "LARP4B" (0.000554)
619. feature "UQCC3" (0.000553)
620. feature "UIMC1" (0.000552)
621. feature "NEDD8" (0.000551)
622. feature "HNRNPM" (0.000551)
623. feature "DBNDD1" (0.000550)
624. feature "MARCHF6" (0.000550)
625. feature "KRT18" (0.000550)
626. feature "PDIA3" (0.000547)
627. feature "ELOB" (0.000545)
628. feature "APLP2" (0.000544)
629. feature "PAK2" (0.000542)
630. feature "TEDC2-AS1" (0.000542)
631. feature "TMEM141" (0.000541)
632. feature "DCTN1" (0.000541)
633. feature "MYH14" (0.000538)
634. feature "RPS29" (0.000537)
635. feature "AP2A1" (0.000534)
636. feature "CENPF" (0.000534)
637. feature "NRBP1" (0.000534)
638. feature "BAZ1B" (0.000533)
639. feature "ZBTB20" (0.000533)
640. feature "NECAB1" (0.000532)
641. feature "SVIL-AS1" (0.000531)
642. feature "MYO5C" (0.000530)
643. feature "UBE2E3" (0.000529)
644. feature "POLR3A" (0.000529)
645. feature "CAPZA1" (0.000527)
646. feature "RPS16" (0.000527)
647. feature "TBC1D9" (0.000526)
648. feature "GPI" (0.000526)
649. feature "CLIC1" (0.000525)
650. feature "FOXK1" (0.000521)
651. feature "RAB5A" (0.000516)
652. feature "LBH" (0.000516)
653. feature "RPL29" (0.000515)
654. feature "RPLP2" (0.000515)
655. feature "CSDE1" (0.000513)
656. feature "DKC1" (0.000512)
657. feature "MYH9" (0.000508)
658. feature "CAMSAP1" (0.000507)
659. feature "BDP1" (0.000507)
660. feature "DPH1" (0.000507)
661. feature "WDR18" (0.000505)
662. feature "CAPNS1" (0.000504)
663. feature "ITPRID2" (0.000503)
664. feature "MT-TY" (0.000503)
665. feature "SMIM10L1" (0.000502)
666. feature "UQCRQ" (0.000501)
667. feature "AAMP" (0.000500)
668. feature "PSMD14" (0.000500)
669. feature "NDUFB2" (0.000499)
670. feature "HSPA5" (0.000498)
671. feature "PRR34-AS1" (0.000497)
672. feature "ILRUN" (0.000496)
673. feature "SOX15" (0.000496)
674. feature "FAM104A" (0.000491)
675. feature "AGBL5" (0.000490)
676. feature "NAGK" (0.000485)
677. feature "TWNK" (0.000484)
678. feature "CDC42BPB" (0.000484)
679. feature "EEF1A1" (0.000483)
680. feature "PCGF3" (0.000482)
681. feature "ITSN2" (0.000481)
682. feature "IGFBP5" (0.000481)
683. feature "SNHG6" (0.000480)
684. feature "RPL28" (0.000480)
685. feature "ZNF580" (0.000480)
686. feature "BCAS3" (0.000480)
687. feature "SMARCB1" (0.000479)
688. feature "MKI67" (0.000479)
689. feature "TMEM160" (0.000479)
690. feature "DEK" (0.000478)
691. feature "BRD4" (0.000477)
692. feature "BPTF" (0.000476)
693. feature "MORN2" (0.000475)
694. feature "PTBP3" (0.000474)
695. feature "HNRNPA2B1" (0.000473)
696. feature "SMIM27" (0.000472)
697. feature "MTMR12" (0.000470)
698. feature "FUBP3" (0.000467)
699. feature "NUDT1" (0.000464)
700. feature "CYB5R3" (0.000464)
701. feature "S100A2" (0.000463)
702. feature "ATP1A1" (0.000462)
703. feature "SALL4" (0.000461)
704. feature "PDAP1" (0.000461)
705. feature "PRMT2" (0.000459)
706. feature "PAPOLA" (0.000459)
707. feature "EIF3A" (0.000458)
708. feature "YWHAB" (0.000458)
709. feature "AP2S1" (0.000457)
710. feature "RPS12" (0.000456)
711. feature "H1-1" (0.000455)
712. feature "INF2" (0.000455)
713. feature "H1-5" (0.000451)
714. feature "RANGAP1" (0.000450)
715. feature "ROCK2" (0.000450)
716. feature "MAPKAPK2" (0.000450)
717. feature "ZBTB45" (0.000449)
718. feature "ATP5ME" (0.000446)
719. feature "TBC1D20" (0.000446)
720. feature "HTATSF1" (0.000445)
721. feature "DNMT1" (0.000445)
722. feature "VMAC" (0.000445)
723. feature "POLR2L" (0.000443)
724. feature "LARP7" (0.000442)
725. feature "HDGF" (0.000442)
726. feature "CTXN1" (0.000442)
727. feature "H1-0" (0.000441)
728. feature "PALLD" (0.000441)
729. feature "SLC25A48" (0.000441)
730. feature "EZR" (0.000440)
731. feature "ARHGEF12" (0.000437)
732. feature "OIP5-AS1" (0.000437)
733. feature "OPTN" (0.000436)
734. feature "EIF4G2" (0.000436)
735. feature "H4C5" (0.000435)
736. feature "ANP32E" (0.000435)
737. feature "HSPB1" (0.000434)
738. feature "LMX1B" (0.000433)
739. feature "HNRNPU" (0.000433)
740. feature "RRP15" (0.000432)
741. feature "SOS1" (0.000432)
742. feature "ATOX1" (0.000432)
743. feature "PHLDB2" (0.000432)
744. feature "TFRC" (0.000431)
745. feature "CAVIN1" (0.000430)
746. feature "PDLIM1" (0.000430)
747. feature "GNL3L" (0.000430)
748. feature "CMIP" (0.000428)
749. feature "CNNM2" (0.000427)
750. feature "ARID5B" (0.000427)
751. feature "JUP" (0.000426)
752. feature "STC2" (0.000425)
753. feature "TARS1" (0.000425)
754. feature "USP32" (0.000423)
755. feature "H19" (0.000423)
756. feature "H1-3" (0.000422)
757. feature "PABPC1" (0.000422)
758. feature "RPL11" (0.000421)
759. feature "C8orf33" (0.000421)
760. feature "EFCAB14" (0.000420)
761. feature "RPL37" (0.000419)
762. feature "CNBP" (0.000418)
763. feature "ZNF2" (0.000417)
764. feature "BROX" (0.000417)
765. feature "ANP32B" (0.000416)
766. feature "NHS" (0.000415)
767. feature "TNRC6A" (0.000414)
768. feature "CBX3" (0.000414)
769. feature "STX2" (0.000413)
770. feature "EHBP1L1" (0.000412)
771. feature "MYO9A" (0.000409)
772. feature "IRAK1" (0.000406)
773. feature "GUK1" (0.000406)
774. feature "PAN3" (0.000405)
775. feature "GPX4" (0.000405)
776. feature "STK16" (0.000404)
777. feature "PTP4A2" (0.000403)
778. feature "CAVIN3" (0.000401)
779. feature "ARHGAP1" (0.000400)
780. feature "KIF14" (0.000399)
781. feature "MSR1" (0.000398)
782. feature "INCENP" (0.000398)
783. feature "NDUFA2" (0.000397)
784. feature "KLF6" (0.000395)
785. feature "SLC2A1" (0.000394)
786. feature "H1-2" (0.000394)
787. feature "NEMP1" (0.000394)
788. feature "CLIP1" (0.000393)
789. feature "SPATS2L" (0.000393)
790. feature "CAMK2N1" (0.000393)
791. feature "TAF15" (0.000393)
792. feature "ID1" (0.000392)
793. feature "RPL12P38" (0.000391)
794. feature "PPIG" (0.000391)
795. feature "MRPS34" (0.000390)
796. feature "QSOX1" (0.000390)
797. feature "RPL27" (0.000388)
798. feature "SUN2" (0.000388)
799. feature "SRSF1" (0.000386)
800. feature "YES1" (0.000385)
801. feature "ARPP19" (0.000384)
802. feature "DDX21" (0.000384)
803. feature "CCDC26" (0.000384)
804. feature "MLYCD" (0.000383)
805. feature "EEA1" (0.000382)
806. feature "HMGB2" (0.000379)
807. feature "DBF4" (0.000379)
808. feature "NMD3" (0.000377)
809. feature "SEPTIN9" (0.000377)
810. feature "ZC3H13" (0.000376)
811. feature "AREG" (0.000375)
812. feature "RAB5C" (0.000372)
813. feature "DTYMK" (0.000371)
814. feature "BRIP1" (0.000370)
815. feature "SIVA1" (0.000368)
816. feature "MRPL33" (0.000366)
817. feature "GADD45A" (0.000366)
818. feature "PPL" (0.000364)
819. feature "YBX3" (0.000364)
820. feature "PDIA6" (0.000363)
821. feature "TUBD1" (0.000361)
822. feature "MAN1A2" (0.000360)
823. feature "JUN" (0.000359)
824. feature "FAM177A1" (0.000357)
825. feature "S100A9" (0.000356)
826. feature "KCNJ2" (0.000354)
827. feature "SKI" (0.000354)
828. feature "PAIP2" (0.000353)
829. feature "ETAA1" (0.000352)
830. feature "PLEKHA4" (0.000351)
831. feature "GDE1" (0.000349)
832. feature "HEPACAM" (0.000349)
833. feature "ZNF512B" (0.000349)
834. feature "EFNA5" (0.000348)
835. feature "TRMT12" (0.000347)
836. feature "SINHCAFP3" (0.000347)
837. feature "ZMIZ1" (0.000344)
838. feature "RAP2B" (0.000344)
839. feature "KRT8" (0.000344)
840. feature "CAPZB" (0.000344)
841. feature "TPX2" (0.000344)
842. feature "ABCA1" (0.000343)
843. feature "EIF4G3" (0.000343)
844. feature "NOTCH2NLC" (0.000342)
845. feature "TNFSF13B" (0.000341)
846. feature "MAP7" (0.000339)
847. feature "NTN4" (0.000338)
848. feature "TCHP" (0.000338)
849. feature "RPL18" (0.000337)
850. feature "DLG5" (0.000337)
851. feature "DAXX" (0.000334)
852. feature "RFFL" (0.000332)
853. feature "MKNK1" (0.000331)
854. feature "EML6" (0.000331)
855. feature "IFI27L2" (0.000329)
856. feature "SYNE2" (0.000329)
857. feature "DHX38" (0.000327)
858. feature "TFE3" (0.000326)
859. feature "RNPS1" (0.000325)
860. feature "RNF34" (0.000325)
861. feature "ABCC4" (0.000324)
862. feature "AP1M1" (0.000324)
863. feature "CPEB4" (0.000323)
864. feature "BAP1" (0.000323)
865. feature "ZNF180" (0.000322)
866. feature "ARHGAP11A" (0.000321)
867. feature "HMGA1" (0.000319)
868. feature "MIR4458HG" (0.000319)
869. feature "ZBTB26" (0.000317)
870. feature "MCM4" (0.000316)
871. feature "FABP5" (0.000315)
872. feature "SLC25A23" (0.000313)
873. feature "ARF3" (0.000311)
874. feature "DNAJC2" (0.000311)
875. feature "GRB10" (0.000310)
876. feature "CSTB" (0.000310)
877. feature "ZC3H18" (0.000310)
878. feature "FBXL16" (0.000310)
879. feature "ROGDI" (0.000309)
880. feature "KLHL8" (0.000309)
881. feature "MORC3" (0.000309)
882. feature "PCBP1" (0.000309)
883. feature "YTHDF1" (0.000308)
884. feature "UBXN2B" (0.000308)
885. feature "TAGLN2" (0.000304)
886. feature "RP9" (0.000302)
887. feature "BTN3A2" (0.000302)
888. feature "CMBL" (0.000302)
889. feature "ZMYND8" (0.000301)
890. feature "RBX1" (0.000301)
891. feature "RPS27L" (0.000300)
892. feature "CS" (0.000298)
893. feature "MT-TE" (0.000298)
894. feature "DAAM1" (0.000297)
895. feature "CDC37" (0.000297)
896. feature "SETD2" (0.000295)
897. feature "CMSS1" (0.000294)
898. feature "HSPBP1" (0.000294)
899. feature "NEUROD2" (0.000292)
900. feature "GSS" (0.000291)
901. feature "ADGRV1" (0.000291)
902. feature "LYPLA2" (0.000291)
903. feature "SART1" (0.000288)
904. feature "TIMM8B" (0.000287)
905. feature "SEMA3D" (0.000282)
906. feature "SHOX" (0.000282)
907. feature "APOOL" (0.000280)
908. feature "EXOC7" (0.000279)
909. feature "FGFR1OP2" (0.000279)
910. feature "RAB40C" (0.000278)
911. feature "NAA10" (0.000277)
912. feature "DNMT3A" (0.000275)
913. feature "CLSPN" (0.000274)
914. feature "ZZEF1" (0.000274)
915. feature "KPNA2" (0.000273)
916. feature "CACNB3" (0.000272)
917. feature "MDC1" (0.000271)
918. feature "MINDY2" (0.000271)
919. feature "PAQR7" (0.000270)
920. feature "METRN" (0.000270)
921. feature "RAD23B" (0.000268)
922. feature "HNRNPUL1" (0.000267)
923. feature "SGO2" (0.000266)
924. feature "RAB1B" (0.000265)
925. feature "PRR5L" (0.000264)
926. feature "CD151" (0.000262)
927. feature "CCNG2" (0.000261)
928. feature "MACC1" (0.000261)
929. feature "C9orf78" (0.000260)
930. feature "SMC4" (0.000260)
931. feature "KDM1A" (0.000259)
932. feature "GPATCH4" (0.000258)
933. feature "TAF3" (0.000257)
934. feature "DUS4L" (0.000256)
935. feature "CCP110" (0.000256)
936. feature "MAFF" (0.000255)
937. feature "SNHG9" (0.000255)
938. feature "GALM" (0.000253)
939. feature "DOHH" (0.000252)
940. feature "MZT2B" (0.000252)
941. feature "FAAP100" (0.000252)
942. feature "FBXW5" (0.000251)
943. feature "SNHG18" (0.000251)
944. feature "MEX3D" (0.000250)
945. feature "TBCA" (0.000250)
946. feature "VRK3" (0.000250)
947. feature "SMAD3" (0.000249)
948. feature "TUBB6" (0.000248)
949. feature "TMEM115" (0.000248)
950. feature "PMEPA1" (0.000247)
951. feature "ELOA" (0.000246)
952. feature "ARHGAP23" (0.000246)
953. feature "HRAS" (0.000245)
954. feature "GCC2" (0.000245)
955. feature "RAB17" (0.000244)
956. feature "NCOR2" (0.000242)
957. feature "NCOA2" (0.000240)
958. feature "NCBP3" (0.000240)
959. feature "HNRNPAB" (0.000240)
960. feature "SEPTIN8" (0.000239)
961. feature "PHACTR2" (0.000238)
962. feature "CKS2" (0.000237)
963. feature "EIF5B" (0.000233)
964. feature "BIK" (0.000231)
965. feature "WDR24" (0.000230)
966. feature "CDKN3" (0.000230)
967. feature "CACNG4" (0.000226)
968. feature "MOB3A" (0.000226)
969. feature "FAM120A" (0.000226)
970. feature "TMEM52B" (0.000224)
971. feature "FAM200B" (0.000222)
972. feature "SPDL1" (0.000221)
973. feature "VPS4A" (0.000220)
974. feature "PPM1G" (0.000220)
975. feature "HMBS" (0.000220)
976. feature "MTUS1" (0.000219)
977. feature "APPL1" (0.000217)
978. feature "TCEAL4" (0.000216)
979. feature "RAB4A" (0.000216)
980. feature "MYO10" (0.000216)
981. feature "S100P" (0.000215)
982. feature "RIPK2" (0.000215)
983. feature "SNRPA" (0.000214)
984. feature "CDAN1" (0.000214)
985. feature "ROCK1" (0.000211)
986. feature "SPATA33" (0.000210)
987. feature "NSD1" (0.000208)
988. feature "HCG11" (0.000207)
989. feature "SPRYD3" (0.000207)
990. feature "CNN3" (0.000206)
991. feature "NIPSNAP3A" (0.000206)
992. feature "MINK1" (0.000205)
993. feature "EID1" (0.000205)
994. feature "GNPAT" (0.000204)
995. feature "BACE2" (0.000203)
996. feature "PATJ" (0.000202)
997. feature "SDHAF3" (0.000202)
998. feature "CASP8AP2" (0.000201)
999. feature "H2AX" (0.000200)
1000. feature "TPH1" (0.000200)
1001. feature "XBP1" (0.000199)
1002. feature "THOC7" (0.000198)
1003. feature "CDK2AP1" (0.000197)
1004. feature "POGK" (0.000195)
1005. feature "ATP2A2" (0.000194)
1006. feature "SNX27" (0.000193)
1007. feature "GNL1" (0.000192)
1008. feature "SFXN3" (0.000188)
1009. feature "CROCC" (0.000188)
1010. feature "HBEGF" (0.000186)
1011. feature "POLK" (0.000185)
1012. feature "PRXL2C" (0.000182)
1013. feature "GABPA" (0.000180)
1014. feature "FAM83A" (0.000180)
1015. feature "VDR" (0.000177)
1016. feature "FEM1A" (0.000174)
1017. feature "SENP3" (0.000173)
1018. feature "AKT1S1" (0.000172)
1019. feature "MZT2A" (0.000169)
1020. feature "YTHDF3" (0.000169)
1021. feature "CMAS" (0.000167)
1022. feature "CAPN1" (0.000166)
1023. feature "YJU2" (0.000166)
1024. feature "SMIM15" (0.000166)
1025. feature "ZNF446" (0.000164)
1026. feature "LCLAT1" (0.000163)
1027. feature "NONO" (0.000163)
1028. feature "SLC25A24" (0.000158)
1029. feature "S100A16" (0.000157)
1030. feature "ZZZ3" (0.000156)
1031. feature "SCLY" (0.000155)
1032. feature "ATP5MC2" (0.000155)
1033. feature "FBXL17" (0.000154)
1034. feature "SMARCC1" (0.000154)
1035. feature "CALB1" (0.000152)
1036. feature "TSSC4" (0.000149)
1037. feature "CCDC167" (0.000146)
1038. feature "HPCAL1" (0.000146)
1039. feature "HSPH1" (0.000141)
1040. feature "GIT1" (0.000141)
1041. feature "TRPV4" (0.000140)
1042. feature "HMG20B" (0.000140)
1043. feature "FAM199X" (0.000138)
1044. feature "SENP6" (0.000137)
1045. feature "BRF1" (0.000136)
1046. feature "KLF9" (0.000136)
1047. feature "DVL3" (0.000133)
1048. feature "CCNY" (0.000131)
1049. feature "EPS8L1" (0.000128)
1050. feature "TAOK3" (0.000127)
1051. feature "MYADM" (0.000126)
1052. feature "SRI" (0.000125)
1053. feature "H2AC16" (0.000125)
1054. feature "GTF3C2" (0.000123)
1055. feature "NPFFR1" (0.000122)
1056. feature "TOP2B" (0.000122)
1057. feature "SLC19A2" (0.000121)
1058. feature "EPAS1" (0.000120)
1059. feature "SBF1" (0.000119)
1060. feature "SETD3" (0.000118)
1061. feature "CTNS" (0.000115)
1062. feature "VAV2" (0.000113)
1063. feature "KIF26A" (0.000112)
1064. feature "LINC01234" (0.000111)
1065. feature "NEFL" (0.000109)
1066. feature "ANXA3" (0.000109)
1067. feature "DPP9" (0.000104)
1068. feature "NSRP1" (0.000103)
1069. feature "STT3A" (0.000101)
1070. feature "EIF1AD" (0.000100)
1071. feature "MAP3K2" (0.000099)
1072. feature "TMEM184C" (0.000099)
1073. feature "FGD5-AS1" (0.000099)
1074. feature "SPA17" (0.000096)
1075. feature "ZBED4" (0.000095)
1076. feature "PRDM2" (0.000094)
1077. feature "IL1R1" (0.000093)
1078. feature "RAB31" (0.000093)
1079. feature "ZC3H4" (0.000092)
1080. feature "ANKRD17" (0.000091)
1081. feature "NRAV" (0.000091)
1082. feature "DTD1" (0.000091)
1083. feature "CDR2L" (0.000088)
1084. feature "NDUFA8" (0.000088)
1085. feature "SIPA1L2" (0.000087)
1086. feature "ATN1" (0.000085)
1087. feature "SENP5" (0.000084)
1088. feature "MYBL2" (0.000082)
1089. feature "LETM1" (0.000080)
1090. feature "RHOBTB2" (0.000080)
1091. feature "PGAM5" (0.000079)
1092. feature "MPHOSPH6" (0.000079)
1093. feature "CDCA4" (0.000077)
1094. feature "S1PR2" (0.000075)
1095. feature "SUDS3" (0.000073)
1096. feature "CIP2A" (0.000071)
1097. feature "LCORL" (0.000067)
1098. feature "HIVEP3" (0.000066)
1099. feature "CLUH" (0.000064)
1100. feature "GPBP1" (0.000060)
1101. feature "NFIC" (0.000060)
1102. feature "CKS1B" (0.000059)
1103. feature "HELB" (0.000057)
1104. feature "TTC19" (0.000054)
1105. feature "ECHDC2" (0.000052)
1106. feature "SNAPC5" (0.000051)
1107. feature "BTRC" (0.000050)
1108. feature "BCAR1" (0.000049)
1109. feature "TF" (0.000048)
1110. feature "C7orf26" (0.000046)
1111. feature "BCCIP" (0.000046)
1112. feature "OXLD1" (0.000046)
1113. feature "PDIA5" (0.000043)
1114. feature "CRKL" (0.000042)
1115. feature "ST8SIA6-AS1" (0.000041)
1116. feature "TCEAL9" (0.000041)
1117. feature "ARHGAP5" (0.000037)
1118. feature "MED13" (0.000036)
1119. feature "ZNF462" (0.000035)
1120. feature "SEPTIN11" (0.000032)
1121. feature "UGDH" (0.000031)
1122. feature "ARHGEF26" (0.000030)
1123. feature "PREX1" (0.000030)
1124. feature "IRGQ" (0.000025)
1125. feature "WDR19" (0.000022)
1126. feature "CCNI" (0.000021)
1127. feature "MYO9B" (0.000017)
1128. feature "ATMIN" (0.000016)
1129. feature "CTHRC1" (0.000015)
1130. feature "UBALD1" (0.000013)
1131. feature "CHAMP1" (0.000012)
1132. feature "AFF4" (0.000011)
1133. feature "TEF" (0.000010)
1134. feature "GPRIN1" (0.000008)
1135. feature "CRK" (0.000008)
1136. feature "NEK2" (0.000006)
1137. feature "SNX8" (0.000005)
1138. feature "NACC2" (0.000004)
1139. feature "RAB11FIP4" (0.000004)
1140. feature "SOX11" (0.000002)
1141. feature "OSBPL6" (0.000000)
1142. feature "PTGER4" (0.000000)
1143. feature "RPS5P3" (0.000000)
1144. feature "FOLR1" (0.000000)
1145. feature "RPSAP36" (0.000000)
1146. feature "TRIM16" (0.000000)
1147. feature "MPV17L2" (0.000000)
1148. feature "DDX60" (0.000000)
1149. feature "F5" (0.000000)
1150. feature "NAGLU" (0.000000)
1151. feature "PHLDA1-AS1" (0.000000)
1152. feature "C9orf163" (0.000000)
1153. feature "STX18-AS1" (0.000000)
1154. feature "PTK7" (0.000000)
1155. feature "CLIP4" (0.000000)
1156. feature "PRR29" (0.000000)
1157. feature "ERVE-1" (0.000000)
1158. feature "RNF43" (0.000000)
1159. feature "FAM43A" (0.000000)
1160. feature "TXNDC2" (0.000000)
1161. feature "KCNMA1" (0.000000)
1162. feature "KCNS3" (0.000000)
1163. feature "LRRTM2" (0.000000)
1164. feature "ZNF333" (0.000000)
1165. feature "CYP2D7" (0.000000)
1166. feature "NR3C1" (0.000000)
1167. feature "APBB2" (0.000000)
1168. feature "PTPRN" (0.000000)
1169. feature "KRT81" (0.000000)
1170. feature "RHPN1" (0.000000)
1171. feature "PPIAP30" (0.000000)
1172. feature "TRPC6" (0.000000)
1173. feature "PPP1R9A" (0.000000)
1174. feature "HLA-DQB1" (0.000000)
1175. feature "LSMEM1" (0.000000)
1176. feature "FBF1" (0.000000)
1177. feature "IFI30" (0.000000)
1178. feature "OLFML2A" (0.000000)
1179. feature "C1orf216" (0.000000)
1180. feature "SLC12A1" (0.000000)
1181. feature "GSTM3" (0.000000)
1182. feature "POU5F1B" (0.000000)
1183. feature "GPT2" (0.000000)
1184. feature "KCNK12" (0.000000)
1185. feature "ZNF256" (0.000000)
1186. feature "LDB3" (0.000000)
1187. feature "NUPR1" (0.000000)
1188. feature "TENT5A" (0.000000)
1189. feature "HSPA7" (0.000000)
1190. feature "RNA5SP155" (0.000000)
1191. feature "TMPPE" (0.000000)
1192. feature "TNFAIP8L3" (0.000000)
1193. feature "SLC14A1" (0.000000)
1194. feature "FERMT1" (0.000000)
1195. feature "ARL6IP1" (0.000000)
1196. feature "ZNF587P1" (0.000000)
1197. feature "PSD3" (0.000000)
1198. feature "IFITM10" (0.000000)
1199. feature "TMEM139" (0.000000)
1200. feature "LINC01237" (0.000000)
1201. feature "MXD4" (0.000000)
1202. feature "TLE6" (0.000000)
1203. feature "MBTPS1" (0.000000)
1204. feature "ACAT2" (0.000000)
1205. feature "TRANK1" (0.000000)
1206. feature "HSPA1L" (0.000000)
1207. feature "CGN" (0.000000)
1208. feature "PKHD1L1" (0.000000)
1209. feature "NEDD9" (0.000000)
1210. feature "EMP1" (0.000000)
1211. feature "TNIK" (0.000000)
1212. feature "TDRD12" (0.000000)
1213. feature "KREMEN2" (0.000000)
1214. feature "SLC9A4" (0.000000)
1215. feature "AMZ1" (0.000000)
1216. feature "C8G" (0.000000)
1217. feature "SPTBN2" (0.000000)
1218. feature "LINC01615" (0.000000)
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1220. feature "TRIM29" (0.000000)
1221. feature "ZNF783" (0.000000)
1222. feature "LINC00466" (0.000000)
1223. feature "CD68" (0.000000)
1224. feature "TPTE2P1" (0.000000)
1225. feature "REEP4" (0.000000)
1226. feature "TRIB2" (0.000000)
1227. feature "SNORD14C" (0.000000)
1228. feature "ZNF804A" (0.000000)
1229. feature "TUBA4A" (0.000000)
1230. feature "ADCY9" (0.000000)
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8588. feature "CUL4B" (0.000000)
8589. feature "CCDC40" (0.000000)
8590. feature "FAM193A" (0.000000)
8591. feature "SAP30-DT" (0.000000)
8592. feature "PAX5" (0.000000)
8593. feature "SMIM1" (0.000000)
8594. feature "OAZ2" (0.000000)
8595. feature "TIGD2" (0.000000)
8596. feature "GAB1" (0.000000)
8597. feature "ABHD14B" (0.000000)
8598. feature "NAPA-AS1" (0.000000)
8599. feature "ARMC5" (0.000000)
8600. feature "ADAM19" (0.000000)
8601. feature "LINC02664" (0.000000)
8602. feature "ENTR1" (0.000000)
8603. feature "FGF8" (0.000000)
8604. feature "HEIH" (0.000000)
8605. feature "PTPN21" (0.000000)
8606. feature "LINC01840" (0.000000)
8607. feature "DET1" (0.000000)
8608. feature "TCL6" (0.000000)
8609. feature "YJEFN3" (0.000000)
8610. feature "ARHGEF33" (0.000000)
8611. feature "PPIAP46" (0.000000)
8612. feature "BRSK2" (0.000000)
8613. feature "FANCF" (0.000000)
8614. feature "ROM1" (0.000000)
8615. feature "SLC9B1" (0.000000)
8616. feature "NRBP2" (0.000000)
8617. feature "MYOSLID" (0.000000)
8618. feature "NFKBIL1" (0.000000)
8619. feature "RPS18P9" (0.000000)
8620. feature "DARS1-AS1" (0.000000)
8621. feature "CSAG3" (0.000000)
8622. feature "MAP3K2-DT" (0.000000)
8623. feature "HOXC6" (0.000000)
8624. feature "MSI1" (0.000000)
8625. feature "TBC1D22A-DT" (0.000000)
8626. feature "CRYZL1" (0.000000)
8627. feature "FAM131C" (0.000000)
8628. feature "FBRSL1" (0.000000)
8629. feature "ANKRD9" (0.000000)
8630. feature "ZNF568" (0.000000)
8631. feature "C5AR1" (0.000000)
8632. feature "ARL15" (0.000000)
8633. feature "NES" (0.000000)
8634. feature "ATP6V0C" (0.000000)
8635. feature "KCTD15" (0.000000)
8636. feature "UBQLN2" (0.000000)
8637. feature "D2HGDH" (0.000000)
8638. feature "MMP25-AS1" (0.000000)
8639. feature "ZNF718" (0.000000)
8640. feature "CCDC51" (0.000000)
8641. feature "FANCE" (0.000000)
8642. feature "SQOR" (0.000000)
8643. feature "HSF2BP" (0.000000)
8644. feature "RRAS" (0.000000)
8645. feature "CYP1B1" (0.000000)

¶

4.6.4) Neural Network

In [ ]:
model3 = GP1()
In [ ]:
X_train_all_nn, X_val_all_nn, y_train_all_nn, y_val_all_nn = train_test_split(X_train_all.values, y_train_all.values, test_size=0.2, random_state=42)


X_train_all_tensor2 = torch.tensor(X_train_all_nn, dtype=torch.float32)
y_train_all_tensor2 = torch.tensor(y_train_all_nn, dtype=torch.float32).unsqueeze(1)
X_val_all_tensor2 = torch.tensor(X_val_all_nn, dtype=torch.float32)
y_val_all_tensor2 = torch.tensor(y_val_all_nn, dtype=torch.float32).unsqueeze(1)  # For binary classification
¶

Training for the model all datasets merged

In [ ]:
# We do the same for the main dataset
optimizer = torch.optim.SGD(model3.parameters(), lr=0.01)
criterion = nn.BCEWithLogitsLoss() # applies a sigmoid activation to the model's raw logits and calculates the binary cross-entropy loss

epochs = 1200
best_val_acc = 0
patience = 3
no_improve_epochs = 0

best_model_path3 = 'best_model_final3.pth'

for epoch in range(epochs):
    model3.train()
    optimizer.zero_grad() # resets the gradients of all model parameters to zero.
    train_output = model3(X_train_all_tensor2)  # Forward pass with all training data, computes the model's predictions (output) for the entire training dataset
    train_loss = criterion(train_output, y_train_all_tensor2) # calculates the difference between predicted outputs and actual labels
    train_loss.backward() #computes the gradients of the loss with respect to the model's parameters
    # This uses backpropagation to distribute the error back through the network
    optimizer.step() #The optimizer applies the selected algorithm (like SGD) to update the weights
    
    # Calculate training accuracy
    with torch.no_grad():
        train_predictions = torch.round(torch.sigmoid(train_output))
        train_correct = (train_predictions == y_train_all_tensor2).sum().item()
        train_total = y_train_all_tensor2.size(0)
        train_acc = train_correct / train_total

    # Validation
    model3.eval() #  sets the model to evaluation mode, disabling features like dropout and batch normalization to ensure consistency
    with torch.no_grad(): #prevents PyTorch from computing gradients during evaluation.
        val_output = model3(X_val_all_tensor2) #computes predictions on the validation dataset
        val_loss = criterion(val_output, y_val_all_tensor2)
        val_predictions = torch.round(torch.sigmoid(val_output))  # converts the logits/predictions into binary predictions (0 or 1)
        val_correct = (val_predictions == y_val_all_tensor2).sum().item()
        val_total = y_val_all_tensor2.size(0)
        val_acc = val_correct / val_total

    print(f'Epoch {epoch+1}/{epochs}, '
          f'Train Loss: {train_loss.item():.4f}, '
          f'Train Accuracy: {train_acc:.4f}, '
          f'Val Loss: {val_loss.item():.4f}, '
          f'Val Accuracy: {val_acc:.4f}')

Epoch 1/1200, Train Loss: 0.6923, Train Accuracy: 0.5104, Val Loss: 0.6933, Val Accuracy: 0.4848
Epoch 2/1200, Train Loss: 0.6922, Train Accuracy: 0.5093, Val Loss: 0.6933, Val Accuracy: 0.4848
Epoch 3/1200, Train Loss: 0.6922, Train Accuracy: 0.5095, Val Loss: 0.6933, Val Accuracy: 0.4848
Epoch 4/1200, Train Loss: 0.6917, Train Accuracy: 0.5160, Val Loss: 0.6933, Val Accuracy: 0.4848
Epoch 5/1200, Train Loss: 0.6915, Train Accuracy: 0.5154, Val Loss: 0.6933, Val Accuracy: 0.4848
Epoch 6/1200, Train Loss: 0.6915, Train Accuracy: 0.5192, Val Loss: 0.6933, Val Accuracy: 0.4848
Epoch 7/1200, Train Loss: 0.6909, Train Accuracy: 0.5218, Val Loss: 0.6933, Val Accuracy: 0.4848
Epoch 8/1200, Train Loss: 0.6910, Train Accuracy: 0.5235, Val Loss: 0.6933, Val Accuracy: 0.4848
Epoch 9/1200, Train Loss: 0.6907, Train Accuracy: 0.5267, Val Loss: 0.6932, Val Accuracy: 0.4848
Epoch 10/1200, Train Loss: 0.6906, Train Accuracy: 0.5275, Val Loss: 0.6932, Val Accuracy: 0.4848
Epoch 11/1200, Train Loss: 0.6901, Train Accuracy: 0.5342, Val Loss: 0.6932, Val Accuracy: 0.4848
Epoch 12/1200, Train Loss: 0.6900, Train Accuracy: 0.5368, Val Loss: 0.6932, Val Accuracy: 0.4848
Epoch 13/1200, Train Loss: 0.6895, Train Accuracy: 0.5367, Val Loss: 0.6932, Val Accuracy: 0.4848
Epoch 14/1200, Train Loss: 0.6893, Train Accuracy: 0.5392, Val Loss: 0.6932, Val Accuracy: 0.4850
Epoch 15/1200, Train Loss: 0.6898, Train Accuracy: 0.5398, Val Loss: 0.6932, Val Accuracy: 0.4850
Epoch 16/1200, Train Loss: 0.6892, Train Accuracy: 0.5369, Val Loss: 0.6932, Val Accuracy: 0.4850
Epoch 17/1200, Train Loss: 0.6895, Train Accuracy: 0.5367, Val Loss: 0.6932, Val Accuracy: 0.4850
Epoch 18/1200, Train Loss: 0.6886, Train Accuracy: 0.5500, Val Loss: 0.6932, Val Accuracy: 0.4850
Epoch 19/1200, Train Loss: 0.6887, Train Accuracy: 0.5387, Val Loss: 0.6932, Val Accuracy: 0.4850
Epoch 20/1200, Train Loss: 0.6886, Train Accuracy: 0.5443, Val Loss: 0.6932, Val Accuracy: 0.4850
Epoch 21/1200, Train Loss: 0.6884, Train Accuracy: 0.5451, Val Loss: 0.6932, Val Accuracy: 0.4850
Epoch 22/1200, Train Loss: 0.6884, Train Accuracy: 0.5461, Val Loss: 0.6932, Val Accuracy: 0.4852
Epoch 23/1200, Train Loss: 0.6878, Train Accuracy: 0.5526, Val Loss: 0.6932, Val Accuracy: 0.4852
Epoch 24/1200, Train Loss: 0.6875, Train Accuracy: 0.5557, Val Loss: 0.6932, Val Accuracy: 0.4856
Epoch 25/1200, Train Loss: 0.6874, Train Accuracy: 0.5549, Val Loss: 0.6931, Val Accuracy: 0.4854
Epoch 26/1200, Train Loss: 0.6871, Train Accuracy: 0.5569, Val Loss: 0.6931, Val Accuracy: 0.4852
Epoch 27/1200, Train Loss: 0.6871, Train Accuracy: 0.5595, Val Loss: 0.6931, Val Accuracy: 0.4852
Epoch 28/1200, Train Loss: 0.6872, Train Accuracy: 0.5600, Val Loss: 0.6931, Val Accuracy: 0.4852
Epoch 29/1200, Train Loss: 0.6868, Train Accuracy: 0.5627, Val Loss: 0.6931, Val Accuracy: 0.4856
Epoch 30/1200, Train Loss: 0.6860, Train Accuracy: 0.5702, Val Loss: 0.6931, Val Accuracy: 0.4858
Epoch 31/1200, Train Loss: 0.6858, Train Accuracy: 0.5722, Val Loss: 0.6931, Val Accuracy: 0.4870
Epoch 32/1200, Train Loss: 0.6864, Train Accuracy: 0.5662, Val Loss: 0.6930, Val Accuracy: 0.4870
Epoch 33/1200, Train Loss: 0.6856, Train Accuracy: 0.5682, Val Loss: 0.6930, Val Accuracy: 0.4895
Epoch 34/1200, Train Loss: 0.6856, Train Accuracy: 0.5721, Val Loss: 0.6930, Val Accuracy: 0.4911
Epoch 35/1200, Train Loss: 0.6851, Train Accuracy: 0.5777, Val Loss: 0.6930, Val Accuracy: 0.4934
Epoch 36/1200, Train Loss: 0.6848, Train Accuracy: 0.5781, Val Loss: 0.6930, Val Accuracy: 0.4961
Epoch 37/1200, Train Loss: 0.6848, Train Accuracy: 0.5787, Val Loss: 0.6929, Val Accuracy: 0.5002
Epoch 38/1200, Train Loss: 0.6845, Train Accuracy: 0.5758, Val Loss: 0.6929, Val Accuracy: 0.5047
Epoch 39/1200, Train Loss: 0.6847, Train Accuracy: 0.5819, Val Loss: 0.6929, Val Accuracy: 0.5087
Epoch 40/1200, Train Loss: 0.6840, Train Accuracy: 0.5874, Val Loss: 0.6928, Val Accuracy: 0.5130
Epoch 41/1200, Train Loss: 0.6840, Train Accuracy: 0.5852, Val Loss: 0.6928, Val Accuracy: 0.5198
Epoch 42/1200, Train Loss: 0.6837, Train Accuracy: 0.5916, Val Loss: 0.6928, Val Accuracy: 0.5268
Epoch 43/1200, Train Loss: 0.6832, Train Accuracy: 0.5901, Val Loss: 0.6927, Val Accuracy: 0.5348
Epoch 44/1200, Train Loss: 0.6833, Train Accuracy: 0.5892, Val Loss: 0.6927, Val Accuracy: 0.5435
Epoch 45/1200, Train Loss: 0.6830, Train Accuracy: 0.5931, Val Loss: 0.6926, Val Accuracy: 0.5513
Epoch 46/1200, Train Loss: 0.6824, Train Accuracy: 0.5986, Val Loss: 0.6926, Val Accuracy: 0.5614
Epoch 47/1200, Train Loss: 0.6824, Train Accuracy: 0.5972, Val Loss: 0.6925, Val Accuracy: 0.5675
Epoch 48/1200, Train Loss: 0.6822, Train Accuracy: 0.5979, Val Loss: 0.6924, Val Accuracy: 0.5772
Epoch 49/1200, Train Loss: 0.6821, Train Accuracy: 0.5979, Val Loss: 0.6924, Val Accuracy: 0.5883
Epoch 50/1200, Train Loss: 0.6815, Train Accuracy: 0.6040, Val Loss: 0.6923, Val Accuracy: 0.5974
Epoch 51/1200, Train Loss: 0.6814, Train Accuracy: 0.6052, Val Loss: 0.6922, Val Accuracy: 0.6061
Epoch 52/1200, Train Loss: 0.6812, Train Accuracy: 0.6056, Val Loss: 0.6921, Val Accuracy: 0.6176
Epoch 53/1200, Train Loss: 0.6807, Train Accuracy: 0.6115, Val Loss: 0.6920, Val Accuracy: 0.6250
Epoch 54/1200, Train Loss: 0.6805, Train Accuracy: 0.6096, Val Loss: 0.6919, Val Accuracy: 0.6363
Epoch 55/1200, Train Loss: 0.6803, Train Accuracy: 0.6158, Val Loss: 0.6918, Val Accuracy: 0.6466
Epoch 56/1200, Train Loss: 0.6800, Train Accuracy: 0.6140, Val Loss: 0.6916, Val Accuracy: 0.6538
Epoch 57/1200, Train Loss: 0.6799, Train Accuracy: 0.6123, Val Loss: 0.6915, Val Accuracy: 0.6614
Epoch 58/1200, Train Loss: 0.6792, Train Accuracy: 0.6230, Val Loss: 0.6913, Val Accuracy: 0.6666
Epoch 59/1200, Train Loss: 0.6791, Train Accuracy: 0.6214, Val Loss: 0.6911, Val Accuracy: 0.6752
Epoch 60/1200, Train Loss: 0.6786, Train Accuracy: 0.6230, Val Loss: 0.6910, Val Accuracy: 0.6810
Epoch 61/1200, Train Loss: 0.6787, Train Accuracy: 0.6186, Val Loss: 0.6908, Val Accuracy: 0.6870
Epoch 62/1200, Train Loss: 0.6783, Train Accuracy: 0.6240, Val Loss: 0.6906, Val Accuracy: 0.6948
Epoch 63/1200, Train Loss: 0.6783, Train Accuracy: 0.6239, Val Loss: 0.6903, Val Accuracy: 0.7006
Epoch 64/1200, Train Loss: 0.6778, Train Accuracy: 0.6300, Val Loss: 0.6901, Val Accuracy: 0.7072
Epoch 65/1200, Train Loss: 0.6772, Train Accuracy: 0.6332, Val Loss: 0.6898, Val Accuracy: 0.7123
Epoch 66/1200, Train Loss: 0.6771, Train Accuracy: 0.6281, Val Loss: 0.6895, Val Accuracy: 0.7164
Epoch 67/1200, Train Loss: 0.6766, Train Accuracy: 0.6344, Val Loss: 0.6892, Val Accuracy: 0.7214
Epoch 68/1200, Train Loss: 0.6765, Train Accuracy: 0.6349, Val Loss: 0.6889, Val Accuracy: 0.7265
Epoch 69/1200, Train Loss: 0.6760, Train Accuracy: 0.6376, Val Loss: 0.6885, Val Accuracy: 0.7333
Epoch 70/1200, Train Loss: 0.6765, Train Accuracy: 0.6350, Val Loss: 0.6881, Val Accuracy: 0.7360
Epoch 71/1200, Train Loss: 0.6755, Train Accuracy: 0.6393, Val Loss: 0.6877, Val Accuracy: 0.7381
Epoch 72/1200, Train Loss: 0.6751, Train Accuracy: 0.6400, Val Loss: 0.6873, Val Accuracy: 0.7422
Epoch 73/1200, Train Loss: 0.6750, Train Accuracy: 0.6457, Val Loss: 0.6869, Val Accuracy: 0.7453
Epoch 74/1200, Train Loss: 0.6744, Train Accuracy: 0.6461, Val Loss: 0.6864, Val Accuracy: 0.7467
Epoch 75/1200, Train Loss: 0.6741, Train Accuracy: 0.6491, Val Loss: 0.6859, Val Accuracy: 0.7481
Epoch 76/1200, Train Loss: 0.6741, Train Accuracy: 0.6453, Val Loss: 0.6853, Val Accuracy: 0.7498
Epoch 77/1200, Train Loss: 0.6739, Train Accuracy: 0.6470, Val Loss: 0.6848, Val Accuracy: 0.7527
Epoch 78/1200, Train Loss: 0.6733, Train Accuracy: 0.6536, Val Loss: 0.6842, Val Accuracy: 0.7560
Epoch 79/1200, Train Loss: 0.6730, Train Accuracy: 0.6538, Val Loss: 0.6836, Val Accuracy: 0.7580
Epoch 80/1200, Train Loss: 0.6729, Train Accuracy: 0.6546, Val Loss: 0.6829, Val Accuracy: 0.7599
Epoch 81/1200, Train Loss: 0.6727, Train Accuracy: 0.6521, Val Loss: 0.6823, Val Accuracy: 0.7617
Epoch 82/1200, Train Loss: 0.6717, Train Accuracy: 0.6585, Val Loss: 0.6816, Val Accuracy: 0.7617
Epoch 83/1200, Train Loss: 0.6711, Train Accuracy: 0.6574, Val Loss: 0.6809, Val Accuracy: 0.7638
Epoch 84/1200, Train Loss: 0.6713, Train Accuracy: 0.6607, Val Loss: 0.6801, Val Accuracy: 0.7654
Epoch 85/1200, Train Loss: 0.6705, Train Accuracy: 0.6666, Val Loss: 0.6794, Val Accuracy: 0.7661
Epoch 86/1200, Train Loss: 0.6698, Train Accuracy: 0.6683, Val Loss: 0.6786, Val Accuracy: 0.7675
Epoch 87/1200, Train Loss: 0.6699, Train Accuracy: 0.6668, Val Loss: 0.6778, Val Accuracy: 0.7683
Epoch 88/1200, Train Loss: 0.6694, Train Accuracy: 0.6654, Val Loss: 0.6770, Val Accuracy: 0.7700
Epoch 89/1200, Train Loss: 0.6690, Train Accuracy: 0.6712, Val Loss: 0.6762, Val Accuracy: 0.7708
Epoch 90/1200, Train Loss: 0.6686, Train Accuracy: 0.6747, Val Loss: 0.6753, Val Accuracy: 0.7712
Epoch 91/1200, Train Loss: 0.6681, Train Accuracy: 0.6737, Val Loss: 0.6745, Val Accuracy: 0.7724
Epoch 92/1200, Train Loss: 0.6678, Train Accuracy: 0.6753, Val Loss: 0.6736, Val Accuracy: 0.7743
Epoch 93/1200, Train Loss: 0.6674, Train Accuracy: 0.6741, Val Loss: 0.6728, Val Accuracy: 0.7759
Epoch 94/1200, Train Loss: 0.6673, Train Accuracy: 0.6767, Val Loss: 0.6719, Val Accuracy: 0.7762
Epoch 95/1200, Train Loss: 0.6666, Train Accuracy: 0.6787, Val Loss: 0.6710, Val Accuracy: 0.7784
Epoch 96/1200, Train Loss: 0.6663, Train Accuracy: 0.6812, Val Loss: 0.6701, Val Accuracy: 0.7794
Epoch 97/1200, Train Loss: 0.6662, Train Accuracy: 0.6741, Val Loss: 0.6693, Val Accuracy: 0.7790
Epoch 98/1200, Train Loss: 0.6658, Train Accuracy: 0.6825, Val Loss: 0.6684, Val Accuracy: 0.7807
Epoch 99/1200, Train Loss: 0.6648, Train Accuracy: 0.6805, Val Loss: 0.6675, Val Accuracy: 0.7819
Epoch 100/1200, Train Loss: 0.6645, Train Accuracy: 0.6864, Val Loss: 0.6666, Val Accuracy: 0.7827
Epoch 101/1200, Train Loss: 0.6638, Train Accuracy: 0.6877, Val Loss: 0.6658, Val Accuracy: 0.7840
Epoch 102/1200, Train Loss: 0.6638, Train Accuracy: 0.6844, Val Loss: 0.6649, Val Accuracy: 0.7840
Epoch 103/1200, Train Loss: 0.6639, Train Accuracy: 0.6890, Val Loss: 0.6641, Val Accuracy: 0.7854
Epoch 104/1200, Train Loss: 0.6631, Train Accuracy: 0.6898, Val Loss: 0.6632, Val Accuracy: 0.7858
Epoch 105/1200, Train Loss: 0.6616, Train Accuracy: 0.6926, Val Loss: 0.6624, Val Accuracy: 0.7864
Epoch 106/1200, Train Loss: 0.6615, Train Accuracy: 0.6949, Val Loss: 0.6616, Val Accuracy: 0.7871
Epoch 107/1200, Train Loss: 0.6609, Train Accuracy: 0.6940, Val Loss: 0.6608, Val Accuracy: 0.7885
Epoch 108/1200, Train Loss: 0.6610, Train Accuracy: 0.6942, Val Loss: 0.6600, Val Accuracy: 0.7887
Epoch 109/1200, Train Loss: 0.6602, Train Accuracy: 0.7030, Val Loss: 0.6592, Val Accuracy: 0.7885
Epoch 110/1200, Train Loss: 0.6600, Train Accuracy: 0.7002, Val Loss: 0.6584, Val Accuracy: 0.7893
Epoch 111/1200, Train Loss: 0.6592, Train Accuracy: 0.7024, Val Loss: 0.6576, Val Accuracy: 0.7902
Epoch 112/1200, Train Loss: 0.6585, Train Accuracy: 0.7032, Val Loss: 0.6567, Val Accuracy: 0.7916
Epoch 113/1200, Train Loss: 0.6581, Train Accuracy: 0.7035, Val Loss: 0.6560, Val Accuracy: 0.7920
Epoch 114/1200, Train Loss: 0.6574, Train Accuracy: 0.7059, Val Loss: 0.6552, Val Accuracy: 0.7916
Epoch 115/1200, Train Loss: 0.6569, Train Accuracy: 0.7052, Val Loss: 0.6544, Val Accuracy: 0.7934
Epoch 116/1200, Train Loss: 0.6567, Train Accuracy: 0.7075, Val Loss: 0.6537, Val Accuracy: 0.7939
Epoch 117/1200, Train Loss: 0.6556, Train Accuracy: 0.7092, Val Loss: 0.6529, Val Accuracy: 0.7937
Epoch 118/1200, Train Loss: 0.6549, Train Accuracy: 0.7088, Val Loss: 0.6521, Val Accuracy: 0.7945
Epoch 119/1200, Train Loss: 0.6541, Train Accuracy: 0.7192, Val Loss: 0.6514, Val Accuracy: 0.7963
Epoch 120/1200, Train Loss: 0.6542, Train Accuracy: 0.7112, Val Loss: 0.6506, Val Accuracy: 0.7961
Epoch 121/1200, Train Loss: 0.6537, Train Accuracy: 0.7134, Val Loss: 0.6498, Val Accuracy: 0.7965
Epoch 122/1200, Train Loss: 0.6530, Train Accuracy: 0.7162, Val Loss: 0.6490, Val Accuracy: 0.7970
Epoch 123/1200, Train Loss: 0.6521, Train Accuracy: 0.7180, Val Loss: 0.6482, Val Accuracy: 0.7969
Epoch 124/1200, Train Loss: 0.6522, Train Accuracy: 0.7127, Val Loss: 0.6474, Val Accuracy: 0.7974
Epoch 125/1200, Train Loss: 0.6513, Train Accuracy: 0.7179, Val Loss: 0.6466, Val Accuracy: 0.7974
Epoch 126/1200, Train Loss: 0.6502, Train Accuracy: 0.7244, Val Loss: 0.6458, Val Accuracy: 0.7978
Epoch 127/1200, Train Loss: 0.6500, Train Accuracy: 0.7213, Val Loss: 0.6450, Val Accuracy: 0.7984
Epoch 128/1200, Train Loss: 0.6493, Train Accuracy: 0.7225, Val Loss: 0.6442, Val Accuracy: 0.7986
Epoch 129/1200, Train Loss: 0.6486, Train Accuracy: 0.7244, Val Loss: 0.6434, Val Accuracy: 0.7992
Epoch 130/1200, Train Loss: 0.6474, Train Accuracy: 0.7291, Val Loss: 0.6426, Val Accuracy: 0.7998
Epoch 131/1200, Train Loss: 0.6472, Train Accuracy: 0.7294, Val Loss: 0.6417, Val Accuracy: 0.8009
Epoch 132/1200, Train Loss: 0.6470, Train Accuracy: 0.7243, Val Loss: 0.6409, Val Accuracy: 0.8017
Epoch 133/1200, Train Loss: 0.6457, Train Accuracy: 0.7294, Val Loss: 0.6400, Val Accuracy: 0.8013
Epoch 134/1200, Train Loss: 0.6456, Train Accuracy: 0.7289, Val Loss: 0.6392, Val Accuracy: 0.8019
Epoch 135/1200, Train Loss: 0.6436, Train Accuracy: 0.7378, Val Loss: 0.6383, Val Accuracy: 0.8015
Epoch 136/1200, Train Loss: 0.6439, Train Accuracy: 0.7299, Val Loss: 0.6375, Val Accuracy: 0.8023
Epoch 137/1200, Train Loss: 0.6428, Train Accuracy: 0.7338, Val Loss: 0.6366, Val Accuracy: 0.8027
Epoch 138/1200, Train Loss: 0.6421, Train Accuracy: 0.7341, Val Loss: 0.6357, Val Accuracy: 0.8027
Epoch 139/1200, Train Loss: 0.6416, Train Accuracy: 0.7349, Val Loss: 0.6349, Val Accuracy: 0.8027
Epoch 140/1200, Train Loss: 0.6403, Train Accuracy: 0.7385, Val Loss: 0.6339, Val Accuracy: 0.8031
Epoch 141/1200, Train Loss: 0.6394, Train Accuracy: 0.7382, Val Loss: 0.6330, Val Accuracy: 0.8035
Epoch 142/1200, Train Loss: 0.6396, Train Accuracy: 0.7383, Val Loss: 0.6321, Val Accuracy: 0.8040
Epoch 143/1200, Train Loss: 0.6383, Train Accuracy: 0.7413, Val Loss: 0.6312, Val Accuracy: 0.8048
Epoch 144/1200, Train Loss: 0.6375, Train Accuracy: 0.7449, Val Loss: 0.6302, Val Accuracy: 0.8052
Epoch 145/1200, Train Loss: 0.6367, Train Accuracy: 0.7447, Val Loss: 0.6293, Val Accuracy: 0.8058
Epoch 146/1200, Train Loss: 0.6360, Train Accuracy: 0.7439, Val Loss: 0.6283, Val Accuracy: 0.8064
Epoch 147/1200, Train Loss: 0.6349, Train Accuracy: 0.7477, Val Loss: 0.6273, Val Accuracy: 0.8075
Epoch 148/1200, Train Loss: 0.6337, Train Accuracy: 0.7476, Val Loss: 0.6264, Val Accuracy: 0.8075
Epoch 149/1200, Train Loss: 0.6337, Train Accuracy: 0.7441, Val Loss: 0.6254, Val Accuracy: 0.8081
Epoch 150/1200, Train Loss: 0.6330, Train Accuracy: 0.7493, Val Loss: 0.6244, Val Accuracy: 0.8087
Epoch 151/1200, Train Loss: 0.6315, Train Accuracy: 0.7527, Val Loss: 0.6234, Val Accuracy: 0.8091
Epoch 152/1200, Train Loss: 0.6307, Train Accuracy: 0.7502, Val Loss: 0.6224, Val Accuracy: 0.8097
Epoch 153/1200, Train Loss: 0.6297, Train Accuracy: 0.7528, Val Loss: 0.6213, Val Accuracy: 0.8099
Epoch 154/1200, Train Loss: 0.6288, Train Accuracy: 0.7499, Val Loss: 0.6203, Val Accuracy: 0.8097
Epoch 155/1200, Train Loss: 0.6280, Train Accuracy: 0.7543, Val Loss: 0.6193, Val Accuracy: 0.8099
Epoch 156/1200, Train Loss: 0.6270, Train Accuracy: 0.7574, Val Loss: 0.6182, Val Accuracy: 0.8101
Epoch 157/1200, Train Loss: 0.6257, Train Accuracy: 0.7605, Val Loss: 0.6171, Val Accuracy: 0.8107
Epoch 158/1200, Train Loss: 0.6251, Train Accuracy: 0.7590, Val Loss: 0.6160, Val Accuracy: 0.8103
Epoch 159/1200, Train Loss: 0.6246, Train Accuracy: 0.7609, Val Loss: 0.6149, Val Accuracy: 0.8108
Epoch 160/1200, Train Loss: 0.6240, Train Accuracy: 0.7565, Val Loss: 0.6137, Val Accuracy: 0.8112
Epoch 161/1200, Train Loss: 0.6229, Train Accuracy: 0.7596, Val Loss: 0.6126, Val Accuracy: 0.8114
Epoch 162/1200, Train Loss: 0.6217, Train Accuracy: 0.7630, Val Loss: 0.6115, Val Accuracy: 0.8122
Epoch 163/1200, Train Loss: 0.6203, Train Accuracy: 0.7646, Val Loss: 0.6103, Val Accuracy: 0.8128
Epoch 164/1200, Train Loss: 0.6194, Train Accuracy: 0.7627, Val Loss: 0.6091, Val Accuracy: 0.8132
Epoch 165/1200, Train Loss: 0.6190, Train Accuracy: 0.7645, Val Loss: 0.6079, Val Accuracy: 0.8132
Epoch 166/1200, Train Loss: 0.6166, Train Accuracy: 0.7694, Val Loss: 0.6067, Val Accuracy: 0.8136
Epoch 167/1200, Train Loss: 0.6163, Train Accuracy: 0.7664, Val Loss: 0.6055, Val Accuracy: 0.8134
Epoch 168/1200, Train Loss: 0.6149, Train Accuracy: 0.7660, Val Loss: 0.6043, Val Accuracy: 0.8142
Epoch 169/1200, Train Loss: 0.6139, Train Accuracy: 0.7692, Val Loss: 0.6031, Val Accuracy: 0.8142
Epoch 170/1200, Train Loss: 0.6130, Train Accuracy: 0.7699, Val Loss: 0.6018, Val Accuracy: 0.8145
Epoch 171/1200, Train Loss: 0.6120, Train Accuracy: 0.7701, Val Loss: 0.6006, Val Accuracy: 0.8151
Epoch 172/1200, Train Loss: 0.6104, Train Accuracy: 0.7732, Val Loss: 0.5993, Val Accuracy: 0.8153
Epoch 173/1200, Train Loss: 0.6094, Train Accuracy: 0.7715, Val Loss: 0.5980, Val Accuracy: 0.8151
Epoch 174/1200, Train Loss: 0.6080, Train Accuracy: 0.7751, Val Loss: 0.5967, Val Accuracy: 0.8155
Epoch 175/1200, Train Loss: 0.6075, Train Accuracy: 0.7758, Val Loss: 0.5954, Val Accuracy: 0.8157
Epoch 176/1200, Train Loss: 0.6064, Train Accuracy: 0.7727, Val Loss: 0.5941, Val Accuracy: 0.8161
Epoch 177/1200, Train Loss: 0.6055, Train Accuracy: 0.7768, Val Loss: 0.5928, Val Accuracy: 0.8165
Epoch 178/1200, Train Loss: 0.6046, Train Accuracy: 0.7752, Val Loss: 0.5914, Val Accuracy: 0.8169
Epoch 179/1200, Train Loss: 0.6029, Train Accuracy: 0.7743, Val Loss: 0.5901, Val Accuracy: 0.8171
Epoch 180/1200, Train Loss: 0.6016, Train Accuracy: 0.7769, Val Loss: 0.5887, Val Accuracy: 0.8173
Epoch 181/1200, Train Loss: 0.6005, Train Accuracy: 0.7771, Val Loss: 0.5874, Val Accuracy: 0.8180
Epoch 182/1200, Train Loss: 0.5988, Train Accuracy: 0.7829, Val Loss: 0.5860, Val Accuracy: 0.8182
Epoch 183/1200, Train Loss: 0.5978, Train Accuracy: 0.7821, Val Loss: 0.5846, Val Accuracy: 0.8184
Epoch 184/1200, Train Loss: 0.5969, Train Accuracy: 0.7844, Val Loss: 0.5832, Val Accuracy: 0.8186
Epoch 185/1200, Train Loss: 0.5958, Train Accuracy: 0.7823, Val Loss: 0.5817, Val Accuracy: 0.8186
Epoch 186/1200, Train Loss: 0.5939, Train Accuracy: 0.7850, Val Loss: 0.5803, Val Accuracy: 0.8198
Epoch 187/1200, Train Loss: 0.5926, Train Accuracy: 0.7875, Val Loss: 0.5789, Val Accuracy: 0.8202
Epoch 188/1200, Train Loss: 0.5915, Train Accuracy: 0.7833, Val Loss: 0.5775, Val Accuracy: 0.8204
Epoch 189/1200, Train Loss: 0.5899, Train Accuracy: 0.7867, Val Loss: 0.5760, Val Accuracy: 0.8208
Epoch 190/1200, Train Loss: 0.5886, Train Accuracy: 0.7882, Val Loss: 0.5746, Val Accuracy: 0.8213
Epoch 191/1200, Train Loss: 0.5879, Train Accuracy: 0.7883, Val Loss: 0.5731, Val Accuracy: 0.8215
Epoch 192/1200, Train Loss: 0.5862, Train Accuracy: 0.7885, Val Loss: 0.5716, Val Accuracy: 0.8221
Epoch 193/1200, Train Loss: 0.5850, Train Accuracy: 0.7875, Val Loss: 0.5700, Val Accuracy: 0.8229
Epoch 194/1200, Train Loss: 0.5841, Train Accuracy: 0.7893, Val Loss: 0.5685, Val Accuracy: 0.8229
Epoch 195/1200, Train Loss: 0.5823, Train Accuracy: 0.7904, Val Loss: 0.5670, Val Accuracy: 0.8229
Epoch 196/1200, Train Loss: 0.5813, Train Accuracy: 0.7922, Val Loss: 0.5654, Val Accuracy: 0.8229
Epoch 197/1200, Train Loss: 0.5793, Train Accuracy: 0.7958, Val Loss: 0.5638, Val Accuracy: 0.8229
Epoch 198/1200, Train Loss: 0.5785, Train Accuracy: 0.7910, Val Loss: 0.5623, Val Accuracy: 0.8233
Epoch 199/1200, Train Loss: 0.5768, Train Accuracy: 0.7936, Val Loss: 0.5607, Val Accuracy: 0.8241
Epoch 200/1200, Train Loss: 0.5752, Train Accuracy: 0.7956, Val Loss: 0.5591, Val Accuracy: 0.8247
Epoch 201/1200, Train Loss: 0.5744, Train Accuracy: 0.7937, Val Loss: 0.5575, Val Accuracy: 0.8262
Epoch 202/1200, Train Loss: 0.5722, Train Accuracy: 0.7937, Val Loss: 0.5560, Val Accuracy: 0.8266
Epoch 203/1200, Train Loss: 0.5710, Train Accuracy: 0.7975, Val Loss: 0.5543, Val Accuracy: 0.8268
Epoch 204/1200, Train Loss: 0.5702, Train Accuracy: 0.7981, Val Loss: 0.5527, Val Accuracy: 0.8270
Epoch 205/1200, Train Loss: 0.5679, Train Accuracy: 0.7980, Val Loss: 0.5511, Val Accuracy: 0.8276
Epoch 206/1200, Train Loss: 0.5669, Train Accuracy: 0.7974, Val Loss: 0.5495, Val Accuracy: 0.8276
Epoch 207/1200, Train Loss: 0.5661, Train Accuracy: 0.8003, Val Loss: 0.5478, Val Accuracy: 0.8289
Epoch 208/1200, Train Loss: 0.5645, Train Accuracy: 0.8012, Val Loss: 0.5462, Val Accuracy: 0.8297
Epoch 209/1200, Train Loss: 0.5627, Train Accuracy: 0.7988, Val Loss: 0.5446, Val Accuracy: 0.8299
Epoch 210/1200, Train Loss: 0.5601, Train Accuracy: 0.8026, Val Loss: 0.5429, Val Accuracy: 0.8297
Epoch 211/1200, Train Loss: 0.5592, Train Accuracy: 0.8082, Val Loss: 0.5413, Val Accuracy: 0.8299
Epoch 212/1200, Train Loss: 0.5578, Train Accuracy: 0.8017, Val Loss: 0.5395, Val Accuracy: 0.8303
Epoch 213/1200, Train Loss: 0.5561, Train Accuracy: 0.8060, Val Loss: 0.5378, Val Accuracy: 0.8307
Epoch 214/1200, Train Loss: 0.5541, Train Accuracy: 0.8082, Val Loss: 0.5361, Val Accuracy: 0.8311
Epoch 215/1200, Train Loss: 0.5524, Train Accuracy: 0.8079, Val Loss: 0.5344, Val Accuracy: 0.8320
Epoch 216/1200, Train Loss: 0.5511, Train Accuracy: 0.8074, Val Loss: 0.5327, Val Accuracy: 0.8326
Epoch 217/1200, Train Loss: 0.5501, Train Accuracy: 0.8096, Val Loss: 0.5310, Val Accuracy: 0.8332
Epoch 218/1200, Train Loss: 0.5491, Train Accuracy: 0.8095, Val Loss: 0.5293, Val Accuracy: 0.8336
Epoch 219/1200, Train Loss: 0.5468, Train Accuracy: 0.8097, Val Loss: 0.5276, Val Accuracy: 0.8340
Epoch 220/1200, Train Loss: 0.5449, Train Accuracy: 0.8095, Val Loss: 0.5258, Val Accuracy: 0.8344
Epoch 221/1200, Train Loss: 0.5431, Train Accuracy: 0.8122, Val Loss: 0.5240, Val Accuracy: 0.8348
Epoch 222/1200, Train Loss: 0.5413, Train Accuracy: 0.8122, Val Loss: 0.5223, Val Accuracy: 0.8351
Epoch 223/1200, Train Loss: 0.5404, Train Accuracy: 0.8100, Val Loss: 0.5205, Val Accuracy: 0.8359
Epoch 224/1200, Train Loss: 0.5398, Train Accuracy: 0.8131, Val Loss: 0.5188, Val Accuracy: 0.8361
Epoch 225/1200, Train Loss: 0.5368, Train Accuracy: 0.8144, Val Loss: 0.5170, Val Accuracy: 0.8365
Epoch 226/1200, Train Loss: 0.5355, Train Accuracy: 0.8152, Val Loss: 0.5152, Val Accuracy: 0.8371
Epoch 227/1200, Train Loss: 0.5334, Train Accuracy: 0.8176, Val Loss: 0.5134, Val Accuracy: 0.8375
Epoch 228/1200, Train Loss: 0.5314, Train Accuracy: 0.8164, Val Loss: 0.5117, Val Accuracy: 0.8383
Epoch 229/1200, Train Loss: 0.5306, Train Accuracy: 0.8158, Val Loss: 0.5099, Val Accuracy: 0.8388
Epoch 230/1200, Train Loss: 0.5286, Train Accuracy: 0.8183, Val Loss: 0.5080, Val Accuracy: 0.8396
Epoch 231/1200, Train Loss: 0.5266, Train Accuracy: 0.8201, Val Loss: 0.5062, Val Accuracy: 0.8400
Epoch 232/1200, Train Loss: 0.5255, Train Accuracy: 0.8195, Val Loss: 0.5043, Val Accuracy: 0.8402
Epoch 233/1200, Train Loss: 0.5238, Train Accuracy: 0.8194, Val Loss: 0.5025, Val Accuracy: 0.8408
Epoch 234/1200, Train Loss: 0.5219, Train Accuracy: 0.8184, Val Loss: 0.5007, Val Accuracy: 0.8414
Epoch 235/1200, Train Loss: 0.5201, Train Accuracy: 0.8222, Val Loss: 0.4989, Val Accuracy: 0.8416
Epoch 236/1200, Train Loss: 0.5192, Train Accuracy: 0.8219, Val Loss: 0.4970, Val Accuracy: 0.8416
Epoch 237/1200, Train Loss: 0.5159, Train Accuracy: 0.8240, Val Loss: 0.4952, Val Accuracy: 0.8421
Epoch 238/1200, Train Loss: 0.5152, Train Accuracy: 0.8231, Val Loss: 0.4934, Val Accuracy: 0.8421
Epoch 239/1200, Train Loss: 0.5131, Train Accuracy: 0.8231, Val Loss: 0.4915, Val Accuracy: 0.8427
Epoch 240/1200, Train Loss: 0.5113, Train Accuracy: 0.8271, Val Loss: 0.4897, Val Accuracy: 0.8429
Epoch 241/1200, Train Loss: 0.5098, Train Accuracy: 0.8224, Val Loss: 0.4878, Val Accuracy: 0.8435
Epoch 242/1200, Train Loss: 0.5081, Train Accuracy: 0.8262, Val Loss: 0.4859, Val Accuracy: 0.8445
Epoch 243/1200, Train Loss: 0.5059, Train Accuracy: 0.8260, Val Loss: 0.4841, Val Accuracy: 0.8456
Epoch 244/1200, Train Loss: 0.5050, Train Accuracy: 0.8262, Val Loss: 0.4821, Val Accuracy: 0.8462
Epoch 245/1200, Train Loss: 0.5031, Train Accuracy: 0.8306, Val Loss: 0.4803, Val Accuracy: 0.8474
Epoch 246/1200, Train Loss: 0.5007, Train Accuracy: 0.8305, Val Loss: 0.4784, Val Accuracy: 0.8482
Epoch 247/1200, Train Loss: 0.4993, Train Accuracy: 0.8307, Val Loss: 0.4766, Val Accuracy: 0.8488
Epoch 248/1200, Train Loss: 0.4974, Train Accuracy: 0.8307, Val Loss: 0.4747, Val Accuracy: 0.8491
Epoch 249/1200, Train Loss: 0.4962, Train Accuracy: 0.8303, Val Loss: 0.4728, Val Accuracy: 0.8497
Epoch 250/1200, Train Loss: 0.4924, Train Accuracy: 0.8335, Val Loss: 0.4709, Val Accuracy: 0.8503
Epoch 251/1200, Train Loss: 0.4920, Train Accuracy: 0.8324, Val Loss: 0.4690, Val Accuracy: 0.8513
Epoch 252/1200, Train Loss: 0.4905, Train Accuracy: 0.8355, Val Loss: 0.4671, Val Accuracy: 0.8521
Epoch 253/1200, Train Loss: 0.4874, Train Accuracy: 0.8349, Val Loss: 0.4652, Val Accuracy: 0.8528
Epoch 254/1200, Train Loss: 0.4862, Train Accuracy: 0.8332, Val Loss: 0.4634, Val Accuracy: 0.8534
Epoch 255/1200, Train Loss: 0.4838, Train Accuracy: 0.8370, Val Loss: 0.4615, Val Accuracy: 0.8544
Epoch 256/1200, Train Loss: 0.4829, Train Accuracy: 0.8373, Val Loss: 0.4595, Val Accuracy: 0.8542
Epoch 257/1200, Train Loss: 0.4807, Train Accuracy: 0.8372, Val Loss: 0.4577, Val Accuracy: 0.8546
Epoch 258/1200, Train Loss: 0.4785, Train Accuracy: 0.8381, Val Loss: 0.4557, Val Accuracy: 0.8550
Epoch 259/1200, Train Loss: 0.4777, Train Accuracy: 0.8385, Val Loss: 0.4538, Val Accuracy: 0.8556
Epoch 260/1200, Train Loss: 0.4753, Train Accuracy: 0.8396, Val Loss: 0.4518, Val Accuracy: 0.8565
Epoch 261/1200, Train Loss: 0.4725, Train Accuracy: 0.8412, Val Loss: 0.4499, Val Accuracy: 0.8581
Epoch 262/1200, Train Loss: 0.4714, Train Accuracy: 0.8427, Val Loss: 0.4479, Val Accuracy: 0.8593
Epoch 263/1200, Train Loss: 0.4682, Train Accuracy: 0.8437, Val Loss: 0.4460, Val Accuracy: 0.8600
Epoch 264/1200, Train Loss: 0.4681, Train Accuracy: 0.8435, Val Loss: 0.4440, Val Accuracy: 0.8606
Epoch 265/1200, Train Loss: 0.4656, Train Accuracy: 0.8434, Val Loss: 0.4421, Val Accuracy: 0.8618
Epoch 266/1200, Train Loss: 0.4631, Train Accuracy: 0.8442, Val Loss: 0.4402, Val Accuracy: 0.8631
Epoch 267/1200, Train Loss: 0.4616, Train Accuracy: 0.8454, Val Loss: 0.4382, Val Accuracy: 0.8629
Epoch 268/1200, Train Loss: 0.4600, Train Accuracy: 0.8478, Val Loss: 0.4362, Val Accuracy: 0.8643
Epoch 269/1200, Train Loss: 0.4570, Train Accuracy: 0.8488, Val Loss: 0.4343, Val Accuracy: 0.8655
Epoch 270/1200, Train Loss: 0.4568, Train Accuracy: 0.8496, Val Loss: 0.4323, Val Accuracy: 0.8661
Epoch 271/1200, Train Loss: 0.4536, Train Accuracy: 0.8489, Val Loss: 0.4303, Val Accuracy: 0.8663
Epoch 272/1200, Train Loss: 0.4525, Train Accuracy: 0.8501, Val Loss: 0.4283, Val Accuracy: 0.8676
Epoch 273/1200, Train Loss: 0.4495, Train Accuracy: 0.8506, Val Loss: 0.4264, Val Accuracy: 0.8684
Epoch 274/1200, Train Loss: 0.4477, Train Accuracy: 0.8522, Val Loss: 0.4244, Val Accuracy: 0.8703
Epoch 275/1200, Train Loss: 0.4459, Train Accuracy: 0.8538, Val Loss: 0.4224, Val Accuracy: 0.8707
Epoch 276/1200, Train Loss: 0.4440, Train Accuracy: 0.8554, Val Loss: 0.4204, Val Accuracy: 0.8715
Epoch 277/1200, Train Loss: 0.4415, Train Accuracy: 0.8551, Val Loss: 0.4183, Val Accuracy: 0.8721
Epoch 278/1200, Train Loss: 0.4413, Train Accuracy: 0.8521, Val Loss: 0.4164, Val Accuracy: 0.8725
Epoch 279/1200, Train Loss: 0.4387, Train Accuracy: 0.8560, Val Loss: 0.4144, Val Accuracy: 0.8733
Epoch 280/1200, Train Loss: 0.4365, Train Accuracy: 0.8565, Val Loss: 0.4124, Val Accuracy: 0.8734
Epoch 281/1200, Train Loss: 0.4335, Train Accuracy: 0.8605, Val Loss: 0.4103, Val Accuracy: 0.8736
Epoch 282/1200, Train Loss: 0.4324, Train Accuracy: 0.8587, Val Loss: 0.4083, Val Accuracy: 0.8744
Epoch 283/1200, Train Loss: 0.4302, Train Accuracy: 0.8597, Val Loss: 0.4063, Val Accuracy: 0.8760
Epoch 284/1200, Train Loss: 0.4286, Train Accuracy: 0.8593, Val Loss: 0.4044, Val Accuracy: 0.8773
Epoch 285/1200, Train Loss: 0.4268, Train Accuracy: 0.8602, Val Loss: 0.4024, Val Accuracy: 0.8775
Epoch 286/1200, Train Loss: 0.4246, Train Accuracy: 0.8609, Val Loss: 0.4004, Val Accuracy: 0.8787
Epoch 287/1200, Train Loss: 0.4228, Train Accuracy: 0.8603, Val Loss: 0.3984, Val Accuracy: 0.8787
Epoch 288/1200, Train Loss: 0.4214, Train Accuracy: 0.8619, Val Loss: 0.3964, Val Accuracy: 0.8793
Epoch 289/1200, Train Loss: 0.4187, Train Accuracy: 0.8628, Val Loss: 0.3943, Val Accuracy: 0.8804
Epoch 290/1200, Train Loss: 0.4160, Train Accuracy: 0.8668, Val Loss: 0.3924, Val Accuracy: 0.8806
Epoch 291/1200, Train Loss: 0.4135, Train Accuracy: 0.8667, Val Loss: 0.3903, Val Accuracy: 0.8818
Epoch 292/1200, Train Loss: 0.4118, Train Accuracy: 0.8668, Val Loss: 0.3883, Val Accuracy: 0.8832
Epoch 293/1200, Train Loss: 0.4102, Train Accuracy: 0.8676, Val Loss: 0.3863, Val Accuracy: 0.8837
Epoch 294/1200, Train Loss: 0.4083, Train Accuracy: 0.8664, Val Loss: 0.3842, Val Accuracy: 0.8847
Epoch 295/1200, Train Loss: 0.4058, Train Accuracy: 0.8691, Val Loss: 0.3823, Val Accuracy: 0.8855
Epoch 296/1200, Train Loss: 0.4041, Train Accuracy: 0.8694, Val Loss: 0.3802, Val Accuracy: 0.8857
Epoch 297/1200, Train Loss: 0.4039, Train Accuracy: 0.8681, Val Loss: 0.3782, Val Accuracy: 0.8865
Epoch 298/1200, Train Loss: 0.4002, Train Accuracy: 0.8726, Val Loss: 0.3761, Val Accuracy: 0.8874
Epoch 299/1200, Train Loss: 0.3982, Train Accuracy: 0.8740, Val Loss: 0.3741, Val Accuracy: 0.8884
Epoch 300/1200, Train Loss: 0.3957, Train Accuracy: 0.8764, Val Loss: 0.3721, Val Accuracy: 0.8886
Epoch 301/1200, Train Loss: 0.3947, Train Accuracy: 0.8736, Val Loss: 0.3701, Val Accuracy: 0.8896
Epoch 302/1200, Train Loss: 0.3912, Train Accuracy: 0.8754, Val Loss: 0.3680, Val Accuracy: 0.8898
Epoch 303/1200, Train Loss: 0.3892, Train Accuracy: 0.8776, Val Loss: 0.3660, Val Accuracy: 0.8904
Epoch 304/1200, Train Loss: 0.3882, Train Accuracy: 0.8774, Val Loss: 0.3639, Val Accuracy: 0.8915
Epoch 305/1200, Train Loss: 0.3847, Train Accuracy: 0.8798, Val Loss: 0.3618, Val Accuracy: 0.8921
Epoch 306/1200, Train Loss: 0.3826, Train Accuracy: 0.8803, Val Loss: 0.3598, Val Accuracy: 0.8927
Epoch 307/1200, Train Loss: 0.3810, Train Accuracy: 0.8792, Val Loss: 0.3578, Val Accuracy: 0.8937
Epoch 308/1200, Train Loss: 0.3777, Train Accuracy: 0.8804, Val Loss: 0.3558, Val Accuracy: 0.8944
Epoch 309/1200, Train Loss: 0.3774, Train Accuracy: 0.8817, Val Loss: 0.3538, Val Accuracy: 0.8958
Epoch 310/1200, Train Loss: 0.3753, Train Accuracy: 0.8808, Val Loss: 0.3518, Val Accuracy: 0.8964
Epoch 311/1200, Train Loss: 0.3730, Train Accuracy: 0.8816, Val Loss: 0.3499, Val Accuracy: 0.8970
Epoch 312/1200, Train Loss: 0.3704, Train Accuracy: 0.8837, Val Loss: 0.3479, Val Accuracy: 0.8970
Epoch 313/1200, Train Loss: 0.3680, Train Accuracy: 0.8859, Val Loss: 0.3458, Val Accuracy: 0.8974
Epoch 314/1200, Train Loss: 0.3681, Train Accuracy: 0.8831, Val Loss: 0.3439, Val Accuracy: 0.8979
Epoch 315/1200, Train Loss: 0.3652, Train Accuracy: 0.8868, Val Loss: 0.3419, Val Accuracy: 0.8985
Epoch 316/1200, Train Loss: 0.3624, Train Accuracy: 0.8863, Val Loss: 0.3400, Val Accuracy: 0.8985
Epoch 317/1200, Train Loss: 0.3613, Train Accuracy: 0.8882, Val Loss: 0.3380, Val Accuracy: 0.8995
Epoch 318/1200, Train Loss: 0.3581, Train Accuracy: 0.8901, Val Loss: 0.3361, Val Accuracy: 0.9005
Epoch 319/1200, Train Loss: 0.3559, Train Accuracy: 0.8880, Val Loss: 0.3341, Val Accuracy: 0.9014
Epoch 320/1200, Train Loss: 0.3542, Train Accuracy: 0.8922, Val Loss: 0.3321, Val Accuracy: 0.9026
Epoch 321/1200, Train Loss: 0.3517, Train Accuracy: 0.8902, Val Loss: 0.3301, Val Accuracy: 0.9032
Epoch 322/1200, Train Loss: 0.3503, Train Accuracy: 0.8935, Val Loss: 0.3281, Val Accuracy: 0.9040
Epoch 323/1200, Train Loss: 0.3469, Train Accuracy: 0.8933, Val Loss: 0.3261, Val Accuracy: 0.9049
Epoch 324/1200, Train Loss: 0.3469, Train Accuracy: 0.8929, Val Loss: 0.3242, Val Accuracy: 0.9061
Epoch 325/1200, Train Loss: 0.3452, Train Accuracy: 0.8957, Val Loss: 0.3222, Val Accuracy: 0.9065
Epoch 326/1200, Train Loss: 0.3425, Train Accuracy: 0.8959, Val Loss: 0.3202, Val Accuracy: 0.9075
Epoch 327/1200, Train Loss: 0.3415, Train Accuracy: 0.8930, Val Loss: 0.3183, Val Accuracy: 0.9082
Epoch 328/1200, Train Loss: 0.3380, Train Accuracy: 0.8975, Val Loss: 0.3164, Val Accuracy: 0.9086
Epoch 329/1200, Train Loss: 0.3362, Train Accuracy: 0.8981, Val Loss: 0.3144, Val Accuracy: 0.9092
Epoch 330/1200, Train Loss: 0.3332, Train Accuracy: 0.8987, Val Loss: 0.3124, Val Accuracy: 0.9098
Epoch 331/1200, Train Loss: 0.3320, Train Accuracy: 0.8999, Val Loss: 0.3105, Val Accuracy: 0.9098
Epoch 332/1200, Train Loss: 0.3327, Train Accuracy: 0.8970, Val Loss: 0.3086, Val Accuracy: 0.9108
Epoch 333/1200, Train Loss: 0.3285, Train Accuracy: 0.8979, Val Loss: 0.3067, Val Accuracy: 0.9110
Epoch 334/1200, Train Loss: 0.3268, Train Accuracy: 0.9003, Val Loss: 0.3049, Val Accuracy: 0.9110
Epoch 335/1200, Train Loss: 0.3242, Train Accuracy: 0.9007, Val Loss: 0.3030, Val Accuracy: 0.9117
Epoch 336/1200, Train Loss: 0.3222, Train Accuracy: 0.9006, Val Loss: 0.3012, Val Accuracy: 0.9127
Epoch 337/1200, Train Loss: 0.3212, Train Accuracy: 0.9032, Val Loss: 0.2994, Val Accuracy: 0.9133
Epoch 338/1200, Train Loss: 0.3177, Train Accuracy: 0.9029, Val Loss: 0.2975, Val Accuracy: 0.9135
Epoch 339/1200, Train Loss: 0.3169, Train Accuracy: 0.9026, Val Loss: 0.2958, Val Accuracy: 0.9139
Epoch 340/1200, Train Loss: 0.3142, Train Accuracy: 0.9056, Val Loss: 0.2940, Val Accuracy: 0.9137
Epoch 341/1200, Train Loss: 0.3123, Train Accuracy: 0.9060, Val Loss: 0.2922, Val Accuracy: 0.9145
Epoch 342/1200, Train Loss: 0.3125, Train Accuracy: 0.9062, Val Loss: 0.2905, Val Accuracy: 0.9147
Epoch 343/1200, Train Loss: 0.3100, Train Accuracy: 0.9063, Val Loss: 0.2887, Val Accuracy: 0.9147
Epoch 344/1200, Train Loss: 0.3095, Train Accuracy: 0.9044, Val Loss: 0.2869, Val Accuracy: 0.9152
Epoch 345/1200, Train Loss: 0.3062, Train Accuracy: 0.9077, Val Loss: 0.2852, Val Accuracy: 0.9158
Epoch 346/1200, Train Loss: 0.3049, Train Accuracy: 0.9069, Val Loss: 0.2835, Val Accuracy: 0.9164
Epoch 347/1200, Train Loss: 0.3027, Train Accuracy: 0.9083, Val Loss: 0.2817, Val Accuracy: 0.9168
Epoch 348/1200, Train Loss: 0.3006, Train Accuracy: 0.9086, Val Loss: 0.2800, Val Accuracy: 0.9172
Epoch 349/1200, Train Loss: 0.2999, Train Accuracy: 0.9088, Val Loss: 0.2784, Val Accuracy: 0.9174
Epoch 350/1200, Train Loss: 0.2975, Train Accuracy: 0.9109, Val Loss: 0.2767, Val Accuracy: 0.9178
Epoch 351/1200, Train Loss: 0.2952, Train Accuracy: 0.9096, Val Loss: 0.2751, Val Accuracy: 0.9184
Epoch 352/1200, Train Loss: 0.2943, Train Accuracy: 0.9099, Val Loss: 0.2735, Val Accuracy: 0.9184
Epoch 353/1200, Train Loss: 0.2933, Train Accuracy: 0.9107, Val Loss: 0.2718, Val Accuracy: 0.9184
Epoch 354/1200, Train Loss: 0.2905, Train Accuracy: 0.9100, Val Loss: 0.2702, Val Accuracy: 0.9191
Epoch 355/1200, Train Loss: 0.2892, Train Accuracy: 0.9123, Val Loss: 0.2687, Val Accuracy: 0.9191
Epoch 356/1200, Train Loss: 0.2875, Train Accuracy: 0.9116, Val Loss: 0.2671, Val Accuracy: 0.9197
Epoch 357/1200, Train Loss: 0.2865, Train Accuracy: 0.9120, Val Loss: 0.2656, Val Accuracy: 0.9197
Epoch 358/1200, Train Loss: 0.2846, Train Accuracy: 0.9150, Val Loss: 0.2641, Val Accuracy: 0.9207
Epoch 359/1200, Train Loss: 0.2819, Train Accuracy: 0.9163, Val Loss: 0.2626, Val Accuracy: 0.9203
Epoch 360/1200, Train Loss: 0.2814, Train Accuracy: 0.9144, Val Loss: 0.2611, Val Accuracy: 0.9203
Epoch 361/1200, Train Loss: 0.2777, Train Accuracy: 0.9150, Val Loss: 0.2596, Val Accuracy: 0.9211
Epoch 362/1200, Train Loss: 0.2772, Train Accuracy: 0.9138, Val Loss: 0.2581, Val Accuracy: 0.9213
Epoch 363/1200, Train Loss: 0.2744, Train Accuracy: 0.9184, Val Loss: 0.2566, Val Accuracy: 0.9215
Epoch 364/1200, Train Loss: 0.2750, Train Accuracy: 0.9166, Val Loss: 0.2551, Val Accuracy: 0.9215
Epoch 365/1200, Train Loss: 0.2714, Train Accuracy: 0.9194, Val Loss: 0.2536, Val Accuracy: 0.9211
Epoch 366/1200, Train Loss: 0.2698, Train Accuracy: 0.9190, Val Loss: 0.2522, Val Accuracy: 0.9211
Epoch 367/1200, Train Loss: 0.2707, Train Accuracy: 0.9168, Val Loss: 0.2508, Val Accuracy: 0.9215
Epoch 368/1200, Train Loss: 0.2678, Train Accuracy: 0.9178, Val Loss: 0.2494, Val Accuracy: 0.9219
Epoch 369/1200, Train Loss: 0.2661, Train Accuracy: 0.9178, Val Loss: 0.2480, Val Accuracy: 0.9219
Epoch 370/1200, Train Loss: 0.2656, Train Accuracy: 0.9191, Val Loss: 0.2466, Val Accuracy: 0.9224
Epoch 371/1200, Train Loss: 0.2631, Train Accuracy: 0.9200, Val Loss: 0.2453, Val Accuracy: 0.9224
Epoch 372/1200, Train Loss: 0.2640, Train Accuracy: 0.9176, Val Loss: 0.2440, Val Accuracy: 0.9224
Epoch 373/1200, Train Loss: 0.2614, Train Accuracy: 0.9197, Val Loss: 0.2427, Val Accuracy: 0.9224
Epoch 374/1200, Train Loss: 0.2604, Train Accuracy: 0.9180, Val Loss: 0.2414, Val Accuracy: 0.9228
Epoch 375/1200, Train Loss: 0.2587, Train Accuracy: 0.9204, Val Loss: 0.2402, Val Accuracy: 0.9226
Epoch 376/1200, Train Loss: 0.2593, Train Accuracy: 0.9195, Val Loss: 0.2389, Val Accuracy: 0.9226
Epoch 377/1200, Train Loss: 0.2552, Train Accuracy: 0.9231, Val Loss: 0.2376, Val Accuracy: 0.9230
Epoch 378/1200, Train Loss: 0.2546, Train Accuracy: 0.9205, Val Loss: 0.2365, Val Accuracy: 0.9238
Epoch 379/1200, Train Loss: 0.2531, Train Accuracy: 0.9211, Val Loss: 0.2352, Val Accuracy: 0.9238
Epoch 380/1200, Train Loss: 0.2517, Train Accuracy: 0.9207, Val Loss: 0.2341, Val Accuracy: 0.9246
Epoch 381/1200, Train Loss: 0.2505, Train Accuracy: 0.9221, Val Loss: 0.2329, Val Accuracy: 0.9250
Epoch 382/1200, Train Loss: 0.2499, Train Accuracy: 0.9219, Val Loss: 0.2317, Val Accuracy: 0.9248
Epoch 383/1200, Train Loss: 0.2473, Train Accuracy: 0.9243, Val Loss: 0.2305, Val Accuracy: 0.9248
Epoch 384/1200, Train Loss: 0.2451, Train Accuracy: 0.9225, Val Loss: 0.2294, Val Accuracy: 0.9253
Epoch 385/1200, Train Loss: 0.2453, Train Accuracy: 0.9248, Val Loss: 0.2283, Val Accuracy: 0.9255
Epoch 386/1200, Train Loss: 0.2446, Train Accuracy: 0.9226, Val Loss: 0.2272, Val Accuracy: 0.9255
Epoch 387/1200, Train Loss: 0.2415, Train Accuracy: 0.9264, Val Loss: 0.2261, Val Accuracy: 0.9255
Epoch 388/1200, Train Loss: 0.2418, Train Accuracy: 0.9247, Val Loss: 0.2250, Val Accuracy: 0.9257
Epoch 389/1200, Train Loss: 0.2393, Train Accuracy: 0.9263, Val Loss: 0.2239, Val Accuracy: 0.9261
Epoch 390/1200, Train Loss: 0.2396, Train Accuracy: 0.9257, Val Loss: 0.2228, Val Accuracy: 0.9265
Epoch 391/1200, Train Loss: 0.2382, Train Accuracy: 0.9245, Val Loss: 0.2218, Val Accuracy: 0.9269
Epoch 392/1200, Train Loss: 0.2370, Train Accuracy: 0.9265, Val Loss: 0.2208, Val Accuracy: 0.9269
Epoch 393/1200, Train Loss: 0.2357, Train Accuracy: 0.9270, Val Loss: 0.2198, Val Accuracy: 0.9275
Epoch 394/1200, Train Loss: 0.2336, Train Accuracy: 0.9275, Val Loss: 0.2188, Val Accuracy: 0.9277
Epoch 395/1200, Train Loss: 0.2339, Train Accuracy: 0.9261, Val Loss: 0.2178, Val Accuracy: 0.9275
Epoch 396/1200, Train Loss: 0.2335, Train Accuracy: 0.9269, Val Loss: 0.2169, Val Accuracy: 0.9273
Epoch 397/1200, Train Loss: 0.2304, Train Accuracy: 0.9293, Val Loss: 0.2159, Val Accuracy: 0.9273
Epoch 398/1200, Train Loss: 0.2307, Train Accuracy: 0.9281, Val Loss: 0.2150, Val Accuracy: 0.9277
Epoch 399/1200, Train Loss: 0.2299, Train Accuracy: 0.9272, Val Loss: 0.2141, Val Accuracy: 0.9279
Epoch 400/1200, Train Loss: 0.2294, Train Accuracy: 0.9265, Val Loss: 0.2131, Val Accuracy: 0.9281
Epoch 401/1200, Train Loss: 0.2270, Train Accuracy: 0.9298, Val Loss: 0.2122, Val Accuracy: 0.9283
Epoch 402/1200, Train Loss: 0.2254, Train Accuracy: 0.9290, Val Loss: 0.2113, Val Accuracy: 0.9283
Epoch 403/1200, Train Loss: 0.2246, Train Accuracy: 0.9298, Val Loss: 0.2104, Val Accuracy: 0.9285
Epoch 404/1200, Train Loss: 0.2232, Train Accuracy: 0.9296, Val Loss: 0.2095, Val Accuracy: 0.9287
Epoch 405/1200, Train Loss: 0.2243, Train Accuracy: 0.9295, Val Loss: 0.2086, Val Accuracy: 0.9288
Epoch 406/1200, Train Loss: 0.2210, Train Accuracy: 0.9302, Val Loss: 0.2077, Val Accuracy: 0.9290
Epoch 407/1200, Train Loss: 0.2215, Train Accuracy: 0.9302, Val Loss: 0.2068, Val Accuracy: 0.9290
Epoch 408/1200, Train Loss: 0.2196, Train Accuracy: 0.9296, Val Loss: 0.2060, Val Accuracy: 0.9287
Epoch 409/1200, Train Loss: 0.2190, Train Accuracy: 0.9307, Val Loss: 0.2051, Val Accuracy: 0.9285
Epoch 410/1200, Train Loss: 0.2180, Train Accuracy: 0.9303, Val Loss: 0.2043, Val Accuracy: 0.9290
Epoch 411/1200, Train Loss: 0.2173, Train Accuracy: 0.9302, Val Loss: 0.2035, Val Accuracy: 0.9298
Epoch 412/1200, Train Loss: 0.2153, Train Accuracy: 0.9303, Val Loss: 0.2027, Val Accuracy: 0.9300
Epoch 413/1200, Train Loss: 0.2155, Train Accuracy: 0.9310, Val Loss: 0.2019, Val Accuracy: 0.9306
Epoch 414/1200, Train Loss: 0.2132, Train Accuracy: 0.9323, Val Loss: 0.2011, Val Accuracy: 0.9308
Epoch 415/1200, Train Loss: 0.2128, Train Accuracy: 0.9324, Val Loss: 0.2003, Val Accuracy: 0.9310
Epoch 416/1200, Train Loss: 0.2118, Train Accuracy: 0.9306, Val Loss: 0.1996, Val Accuracy: 0.9312
Epoch 417/1200, Train Loss: 0.2120, Train Accuracy: 0.9327, Val Loss: 0.1989, Val Accuracy: 0.9312
Epoch 418/1200, Train Loss: 0.2111, Train Accuracy: 0.9337, Val Loss: 0.1981, Val Accuracy: 0.9310
Epoch 419/1200, Train Loss: 0.2090, Train Accuracy: 0.9328, Val Loss: 0.1974, Val Accuracy: 0.9310
Epoch 420/1200, Train Loss: 0.2071, Train Accuracy: 0.9339, Val Loss: 0.1966, Val Accuracy: 0.9310
Epoch 421/1200, Train Loss: 0.2090, Train Accuracy: 0.9321, Val Loss: 0.1959, Val Accuracy: 0.9310
Epoch 422/1200, Train Loss: 0.2087, Train Accuracy: 0.9310, Val Loss: 0.1953, Val Accuracy: 0.9312
Epoch 423/1200, Train Loss: 0.2077, Train Accuracy: 0.9330, Val Loss: 0.1946, Val Accuracy: 0.9314
Epoch 424/1200, Train Loss: 0.2070, Train Accuracy: 0.9338, Val Loss: 0.1939, Val Accuracy: 0.9314
Epoch 425/1200, Train Loss: 0.2060, Train Accuracy: 0.9325, Val Loss: 0.1933, Val Accuracy: 0.9314
Epoch 426/1200, Train Loss: 0.2045, Train Accuracy: 0.9340, Val Loss: 0.1927, Val Accuracy: 0.9316
Epoch 427/1200, Train Loss: 0.2029, Train Accuracy: 0.9343, Val Loss: 0.1920, Val Accuracy: 0.9318
Epoch 428/1200, Train Loss: 0.2026, Train Accuracy: 0.9331, Val Loss: 0.1914, Val Accuracy: 0.9320
Epoch 429/1200, Train Loss: 0.2007, Train Accuracy: 0.9348, Val Loss: 0.1907, Val Accuracy: 0.9325
Epoch 430/1200, Train Loss: 0.2017, Train Accuracy: 0.9341, Val Loss: 0.1901, Val Accuracy: 0.9327
Epoch 431/1200, Train Loss: 0.2007, Train Accuracy: 0.9349, Val Loss: 0.1895, Val Accuracy: 0.9327
Epoch 432/1200, Train Loss: 0.1986, Train Accuracy: 0.9359, Val Loss: 0.1888, Val Accuracy: 0.9331
Epoch 433/1200, Train Loss: 0.1989, Train Accuracy: 0.9355, Val Loss: 0.1882, Val Accuracy: 0.9331
Epoch 434/1200, Train Loss: 0.1969, Train Accuracy: 0.9355, Val Loss: 0.1876, Val Accuracy: 0.9333
Epoch 435/1200, Train Loss: 0.1969, Train Accuracy: 0.9358, Val Loss: 0.1871, Val Accuracy: 0.9333
Epoch 436/1200, Train Loss: 0.1963, Train Accuracy: 0.9357, Val Loss: 0.1865, Val Accuracy: 0.9333
Epoch 437/1200, Train Loss: 0.1945, Train Accuracy: 0.9347, Val Loss: 0.1859, Val Accuracy: 0.9333
Epoch 438/1200, Train Loss: 0.1963, Train Accuracy: 0.9345, Val Loss: 0.1853, Val Accuracy: 0.9333
Epoch 439/1200, Train Loss: 0.1939, Train Accuracy: 0.9355, Val Loss: 0.1848, Val Accuracy: 0.9335
Epoch 440/1200, Train Loss: 0.1934, Train Accuracy: 0.9352, Val Loss: 0.1842, Val Accuracy: 0.9339
Epoch 441/1200, Train Loss: 0.1924, Train Accuracy: 0.9350, Val Loss: 0.1836, Val Accuracy: 0.9341
Epoch 442/1200, Train Loss: 0.1925, Train Accuracy: 0.9369, Val Loss: 0.1831, Val Accuracy: 0.9345
Epoch 443/1200, Train Loss: 0.1926, Train Accuracy: 0.9359, Val Loss: 0.1826, Val Accuracy: 0.9345
Epoch 444/1200, Train Loss: 0.1897, Train Accuracy: 0.9357, Val Loss: 0.1820, Val Accuracy: 0.9345
Epoch 445/1200, Train Loss: 0.1900, Train Accuracy: 0.9362, Val Loss: 0.1815, Val Accuracy: 0.9345
Epoch 446/1200, Train Loss: 0.1894, Train Accuracy: 0.9375, Val Loss: 0.1810, Val Accuracy: 0.9345
Epoch 447/1200, Train Loss: 0.1888, Train Accuracy: 0.9365, Val Loss: 0.1804, Val Accuracy: 0.9345
Epoch 448/1200, Train Loss: 0.1881, Train Accuracy: 0.9383, Val Loss: 0.1800, Val Accuracy: 0.9345
Epoch 449/1200, Train Loss: 0.1879, Train Accuracy: 0.9361, Val Loss: 0.1795, Val Accuracy: 0.9343
Epoch 450/1200, Train Loss: 0.1862, Train Accuracy: 0.9384, Val Loss: 0.1790, Val Accuracy: 0.9345
Epoch 451/1200, Train Loss: 0.1855, Train Accuracy: 0.9381, Val Loss: 0.1785, Val Accuracy: 0.9345
Epoch 452/1200, Train Loss: 0.1845, Train Accuracy: 0.9385, Val Loss: 0.1780, Val Accuracy: 0.9347
Epoch 453/1200, Train Loss: 0.1860, Train Accuracy: 0.9369, Val Loss: 0.1775, Val Accuracy: 0.9347
Epoch 454/1200, Train Loss: 0.1844, Train Accuracy: 0.9373, Val Loss: 0.1771, Val Accuracy: 0.9347
Epoch 455/1200, Train Loss: 0.1853, Train Accuracy: 0.9367, Val Loss: 0.1766, Val Accuracy: 0.9353
Epoch 456/1200, Train Loss: 0.1828, Train Accuracy: 0.9389, Val Loss: 0.1762, Val Accuracy: 0.9351
Epoch 457/1200, Train Loss: 0.1811, Train Accuracy: 0.9387, Val Loss: 0.1757, Val Accuracy: 0.9353
Epoch 458/1200, Train Loss: 0.1811, Train Accuracy: 0.9374, Val Loss: 0.1753, Val Accuracy: 0.9353
Epoch 459/1200, Train Loss: 0.1824, Train Accuracy: 0.9389, Val Loss: 0.1749, Val Accuracy: 0.9349
Epoch 460/1200, Train Loss: 0.1789, Train Accuracy: 0.9393, Val Loss: 0.1744, Val Accuracy: 0.9353
Epoch 461/1200, Train Loss: 0.1804, Train Accuracy: 0.9391, Val Loss: 0.1740, Val Accuracy: 0.9353
Epoch 462/1200, Train Loss: 0.1788, Train Accuracy: 0.9401, Val Loss: 0.1735, Val Accuracy: 0.9353
Epoch 463/1200, Train Loss: 0.1800, Train Accuracy: 0.9396, Val Loss: 0.1731, Val Accuracy: 0.9355
Epoch 464/1200, Train Loss: 0.1774, Train Accuracy: 0.9404, Val Loss: 0.1727, Val Accuracy: 0.9355
Epoch 465/1200, Train Loss: 0.1776, Train Accuracy: 0.9394, Val Loss: 0.1722, Val Accuracy: 0.9358
Epoch 466/1200, Train Loss: 0.1767, Train Accuracy: 0.9403, Val Loss: 0.1718, Val Accuracy: 0.9358
Epoch 467/1200, Train Loss: 0.1755, Train Accuracy: 0.9428, Val Loss: 0.1714, Val Accuracy: 0.9358
Epoch 468/1200, Train Loss: 0.1745, Train Accuracy: 0.9418, Val Loss: 0.1710, Val Accuracy: 0.9360
Epoch 469/1200, Train Loss: 0.1761, Train Accuracy: 0.9407, Val Loss: 0.1706, Val Accuracy: 0.9364
Epoch 470/1200, Train Loss: 0.1732, Train Accuracy: 0.9427, Val Loss: 0.1702, Val Accuracy: 0.9368
Epoch 471/1200, Train Loss: 0.1748, Train Accuracy: 0.9405, Val Loss: 0.1698, Val Accuracy: 0.9368
Epoch 472/1200, Train Loss: 0.1730, Train Accuracy: 0.9427, Val Loss: 0.1694, Val Accuracy: 0.9368
Epoch 473/1200, Train Loss: 0.1741, Train Accuracy: 0.9401, Val Loss: 0.1691, Val Accuracy: 0.9372
Epoch 474/1200, Train Loss: 0.1731, Train Accuracy: 0.9410, Val Loss: 0.1687, Val Accuracy: 0.9374
Epoch 475/1200, Train Loss: 0.1717, Train Accuracy: 0.9419, Val Loss: 0.1683, Val Accuracy: 0.9374
Epoch 476/1200, Train Loss: 0.1739, Train Accuracy: 0.9408, Val Loss: 0.1679, Val Accuracy: 0.9372
Epoch 477/1200, Train Loss: 0.1709, Train Accuracy: 0.9417, Val Loss: 0.1676, Val Accuracy: 0.9374
Epoch 478/1200, Train Loss: 0.1711, Train Accuracy: 0.9426, Val Loss: 0.1672, Val Accuracy: 0.9374
Epoch 479/1200, Train Loss: 0.1709, Train Accuracy: 0.9415, Val Loss: 0.1668, Val Accuracy: 0.9376
Epoch 480/1200, Train Loss: 0.1697, Train Accuracy: 0.9419, Val Loss: 0.1665, Val Accuracy: 0.9378
Epoch 481/1200, Train Loss: 0.1699, Train Accuracy: 0.9421, Val Loss: 0.1661, Val Accuracy: 0.9382
Epoch 482/1200, Train Loss: 0.1682, Train Accuracy: 0.9431, Val Loss: 0.1658, Val Accuracy: 0.9382
Epoch 483/1200, Train Loss: 0.1672, Train Accuracy: 0.9427, Val Loss: 0.1654, Val Accuracy: 0.9384
Epoch 484/1200, Train Loss: 0.1686, Train Accuracy: 0.9413, Val Loss: 0.1651, Val Accuracy: 0.9390
Epoch 485/1200, Train Loss: 0.1661, Train Accuracy: 0.9446, Val Loss: 0.1647, Val Accuracy: 0.9390
Epoch 486/1200, Train Loss: 0.1658, Train Accuracy: 0.9440, Val Loss: 0.1644, Val Accuracy: 0.9392
Epoch 487/1200, Train Loss: 0.1666, Train Accuracy: 0.9424, Val Loss: 0.1641, Val Accuracy: 0.9392
Epoch 488/1200, Train Loss: 0.1664, Train Accuracy: 0.9429, Val Loss: 0.1637, Val Accuracy: 0.9392
Epoch 489/1200, Train Loss: 0.1674, Train Accuracy: 0.9414, Val Loss: 0.1634, Val Accuracy: 0.9393
Epoch 490/1200, Train Loss: 0.1658, Train Accuracy: 0.9421, Val Loss: 0.1631, Val Accuracy: 0.9393
Epoch 491/1200, Train Loss: 0.1643, Train Accuracy: 0.9446, Val Loss: 0.1628, Val Accuracy: 0.9393
Epoch 492/1200, Train Loss: 0.1632, Train Accuracy: 0.9442, Val Loss: 0.1625, Val Accuracy: 0.9393
Epoch 493/1200, Train Loss: 0.1634, Train Accuracy: 0.9445, Val Loss: 0.1622, Val Accuracy: 0.9393
Epoch 494/1200, Train Loss: 0.1642, Train Accuracy: 0.9437, Val Loss: 0.1619, Val Accuracy: 0.9393
Epoch 495/1200, Train Loss: 0.1622, Train Accuracy: 0.9435, Val Loss: 0.1616, Val Accuracy: 0.9393
Epoch 496/1200, Train Loss: 0.1607, Train Accuracy: 0.9436, Val Loss: 0.1613, Val Accuracy: 0.9393
Epoch 497/1200, Train Loss: 0.1632, Train Accuracy: 0.9440, Val Loss: 0.1610, Val Accuracy: 0.9392
Epoch 498/1200, Train Loss: 0.1600, Train Accuracy: 0.9443, Val Loss: 0.1607, Val Accuracy: 0.9393
Epoch 499/1200, Train Loss: 0.1596, Train Accuracy: 0.9460, Val Loss: 0.1604, Val Accuracy: 0.9392
Epoch 500/1200, Train Loss: 0.1599, Train Accuracy: 0.9450, Val Loss: 0.1601, Val Accuracy: 0.9392
Epoch 501/1200, Train Loss: 0.1590, Train Accuracy: 0.9452, Val Loss: 0.1598, Val Accuracy: 0.9392
Epoch 502/1200, Train Loss: 0.1599, Train Accuracy: 0.9458, Val Loss: 0.1595, Val Accuracy: 0.9393
Epoch 503/1200, Train Loss: 0.1601, Train Accuracy: 0.9454, Val Loss: 0.1593, Val Accuracy: 0.9395
Epoch 504/1200, Train Loss: 0.1608, Train Accuracy: 0.9449, Val Loss: 0.1590, Val Accuracy: 0.9393
Epoch 505/1200, Train Loss: 0.1581, Train Accuracy: 0.9437, Val Loss: 0.1587, Val Accuracy: 0.9395
Epoch 506/1200, Train Loss: 0.1586, Train Accuracy: 0.9455, Val Loss: 0.1585, Val Accuracy: 0.9393
Epoch 507/1200, Train Loss: 0.1561, Train Accuracy: 0.9466, Val Loss: 0.1582, Val Accuracy: 0.9395
Epoch 508/1200, Train Loss: 0.1585, Train Accuracy: 0.9455, Val Loss: 0.1579, Val Accuracy: 0.9395
Epoch 509/1200, Train Loss: 0.1570, Train Accuracy: 0.9471, Val Loss: 0.1577, Val Accuracy: 0.9395
Epoch 510/1200, Train Loss: 0.1557, Train Accuracy: 0.9459, Val Loss: 0.1574, Val Accuracy: 0.9395
Epoch 511/1200, Train Loss: 0.1545, Train Accuracy: 0.9467, Val Loss: 0.1571, Val Accuracy: 0.9395
Epoch 512/1200, Train Loss: 0.1548, Train Accuracy: 0.9473, Val Loss: 0.1568, Val Accuracy: 0.9395
Epoch 513/1200, Train Loss: 0.1541, Train Accuracy: 0.9466, Val Loss: 0.1566, Val Accuracy: 0.9397
Epoch 514/1200, Train Loss: 0.1546, Train Accuracy: 0.9476, Val Loss: 0.1563, Val Accuracy: 0.9395
Epoch 515/1200, Train Loss: 0.1524, Train Accuracy: 0.9476, Val Loss: 0.1561, Val Accuracy: 0.9399
Epoch 516/1200, Train Loss: 0.1541, Train Accuracy: 0.9460, Val Loss: 0.1558, Val Accuracy: 0.9399
Epoch 517/1200, Train Loss: 0.1523, Train Accuracy: 0.9477, Val Loss: 0.1556, Val Accuracy: 0.9401
Epoch 518/1200, Train Loss: 0.1518, Train Accuracy: 0.9481, Val Loss: 0.1553, Val Accuracy: 0.9403
Epoch 519/1200, Train Loss: 0.1525, Train Accuracy: 0.9484, Val Loss: 0.1551, Val Accuracy: 0.9399
Epoch 520/1200, Train Loss: 0.1507, Train Accuracy: 0.9477, Val Loss: 0.1549, Val Accuracy: 0.9403
Epoch 521/1200, Train Loss: 0.1521, Train Accuracy: 0.9464, Val Loss: 0.1546, Val Accuracy: 0.9405
Epoch 522/1200, Train Loss: 0.1521, Train Accuracy: 0.9464, Val Loss: 0.1544, Val Accuracy: 0.9407
Epoch 523/1200, Train Loss: 0.1510, Train Accuracy: 0.9477, Val Loss: 0.1541, Val Accuracy: 0.9409
Epoch 524/1200, Train Loss: 0.1502, Train Accuracy: 0.9491, Val Loss: 0.1539, Val Accuracy: 0.9409
Epoch 525/1200, Train Loss: 0.1506, Train Accuracy: 0.9465, Val Loss: 0.1537, Val Accuracy: 0.9409
Epoch 526/1200, Train Loss: 0.1489, Train Accuracy: 0.9495, Val Loss: 0.1534, Val Accuracy: 0.9409
Epoch 527/1200, Train Loss: 0.1478, Train Accuracy: 0.9488, Val Loss: 0.1532, Val Accuracy: 0.9407
Epoch 528/1200, Train Loss: 0.1466, Train Accuracy: 0.9507, Val Loss: 0.1530, Val Accuracy: 0.9409
Epoch 529/1200, Train Loss: 0.1483, Train Accuracy: 0.9487, Val Loss: 0.1528, Val Accuracy: 0.9411
Epoch 530/1200, Train Loss: 0.1460, Train Accuracy: 0.9518, Val Loss: 0.1525, Val Accuracy: 0.9409
Epoch 531/1200, Train Loss: 0.1474, Train Accuracy: 0.9493, Val Loss: 0.1523, Val Accuracy: 0.9411
Epoch 532/1200, Train Loss: 0.1475, Train Accuracy: 0.9477, Val Loss: 0.1521, Val Accuracy: 0.9415
Epoch 533/1200, Train Loss: 0.1465, Train Accuracy: 0.9502, Val Loss: 0.1519, Val Accuracy: 0.9413
Epoch 534/1200, Train Loss: 0.1465, Train Accuracy: 0.9484, Val Loss: 0.1516, Val Accuracy: 0.9411
Epoch 535/1200, Train Loss: 0.1467, Train Accuracy: 0.9483, Val Loss: 0.1514, Val Accuracy: 0.9411
Epoch 536/1200, Train Loss: 0.1444, Train Accuracy: 0.9500, Val Loss: 0.1512, Val Accuracy: 0.9411
Epoch 537/1200, Train Loss: 0.1445, Train Accuracy: 0.9505, Val Loss: 0.1510, Val Accuracy: 0.9411
Epoch 538/1200, Train Loss: 0.1431, Train Accuracy: 0.9503, Val Loss: 0.1508, Val Accuracy: 0.9417
Epoch 539/1200, Train Loss: 0.1443, Train Accuracy: 0.9508, Val Loss: 0.1506, Val Accuracy: 0.9415
Epoch 540/1200, Train Loss: 0.1433, Train Accuracy: 0.9502, Val Loss: 0.1504, Val Accuracy: 0.9415
Epoch 541/1200, Train Loss: 0.1438, Train Accuracy: 0.9500, Val Loss: 0.1502, Val Accuracy: 0.9415
Epoch 542/1200, Train Loss: 0.1431, Train Accuracy: 0.9504, Val Loss: 0.1500, Val Accuracy: 0.9415
Epoch 543/1200, Train Loss: 0.1411, Train Accuracy: 0.9512, Val Loss: 0.1498, Val Accuracy: 0.9417
Epoch 544/1200, Train Loss: 0.1419, Train Accuracy: 0.9506, Val Loss: 0.1496, Val Accuracy: 0.9417
Epoch 545/1200, Train Loss: 0.1428, Train Accuracy: 0.9500, Val Loss: 0.1494, Val Accuracy: 0.9417
Epoch 546/1200, Train Loss: 0.1397, Train Accuracy: 0.9514, Val Loss: 0.1492, Val Accuracy: 0.9417
Epoch 547/1200, Train Loss: 0.1426, Train Accuracy: 0.9495, Val Loss: 0.1490, Val Accuracy: 0.9417
Epoch 548/1200, Train Loss: 0.1416, Train Accuracy: 0.9516, Val Loss: 0.1488, Val Accuracy: 0.9417
Epoch 549/1200, Train Loss: 0.1405, Train Accuracy: 0.9512, Val Loss: 0.1486, Val Accuracy: 0.9417
Epoch 550/1200, Train Loss: 0.1404, Train Accuracy: 0.9519, Val Loss: 0.1485, Val Accuracy: 0.9417
Epoch 551/1200, Train Loss: 0.1389, Train Accuracy: 0.9523, Val Loss: 0.1483, Val Accuracy: 0.9419
Epoch 552/1200, Train Loss: 0.1393, Train Accuracy: 0.9518, Val Loss: 0.1481, Val Accuracy: 0.9419
Epoch 553/1200, Train Loss: 0.1398, Train Accuracy: 0.9521, Val Loss: 0.1479, Val Accuracy: 0.9417
Epoch 554/1200, Train Loss: 0.1394, Train Accuracy: 0.9519, Val Loss: 0.1477, Val Accuracy: 0.9421
Epoch 555/1200, Train Loss: 0.1393, Train Accuracy: 0.9512, Val Loss: 0.1476, Val Accuracy: 0.9421
Epoch 556/1200, Train Loss: 0.1386, Train Accuracy: 0.9512, Val Loss: 0.1474, Val Accuracy: 0.9421
Epoch 557/1200, Train Loss: 0.1374, Train Accuracy: 0.9533, Val Loss: 0.1472, Val Accuracy: 0.9423
Epoch 558/1200, Train Loss: 0.1382, Train Accuracy: 0.9515, Val Loss: 0.1470, Val Accuracy: 0.9423
Epoch 559/1200, Train Loss: 0.1375, Train Accuracy: 0.9531, Val Loss: 0.1469, Val Accuracy: 0.9425
Epoch 560/1200, Train Loss: 0.1398, Train Accuracy: 0.9527, Val Loss: 0.1467, Val Accuracy: 0.9425
Epoch 561/1200, Train Loss: 0.1372, Train Accuracy: 0.9516, Val Loss: 0.1465, Val Accuracy: 0.9421
Epoch 562/1200, Train Loss: 0.1358, Train Accuracy: 0.9526, Val Loss: 0.1464, Val Accuracy: 0.9421
Epoch 563/1200, Train Loss: 0.1379, Train Accuracy: 0.9523, Val Loss: 0.1462, Val Accuracy: 0.9423
Epoch 564/1200, Train Loss: 0.1344, Train Accuracy: 0.9545, Val Loss: 0.1460, Val Accuracy: 0.9419
Epoch 565/1200, Train Loss: 0.1360, Train Accuracy: 0.9528, Val Loss: 0.1458, Val Accuracy: 0.9423
Epoch 566/1200, Train Loss: 0.1355, Train Accuracy: 0.9529, Val Loss: 0.1457, Val Accuracy: 0.9423
Epoch 567/1200, Train Loss: 0.1338, Train Accuracy: 0.9536, Val Loss: 0.1455, Val Accuracy: 0.9423
Epoch 568/1200, Train Loss: 0.1345, Train Accuracy: 0.9532, Val Loss: 0.1453, Val Accuracy: 0.9423
Epoch 569/1200, Train Loss: 0.1348, Train Accuracy: 0.9519, Val Loss: 0.1452, Val Accuracy: 0.9421
Epoch 570/1200, Train Loss: 0.1324, Train Accuracy: 0.9531, Val Loss: 0.1450, Val Accuracy: 0.9423
Epoch 571/1200, Train Loss: 0.1354, Train Accuracy: 0.9552, Val Loss: 0.1448, Val Accuracy: 0.9425
Epoch 572/1200, Train Loss: 0.1330, Train Accuracy: 0.9534, Val Loss: 0.1447, Val Accuracy: 0.9423
Epoch 573/1200, Train Loss: 0.1329, Train Accuracy: 0.9534, Val Loss: 0.1445, Val Accuracy: 0.9423
Epoch 574/1200, Train Loss: 0.1327, Train Accuracy: 0.9547, Val Loss: 0.1444, Val Accuracy: 0.9421
Epoch 575/1200, Train Loss: 0.1333, Train Accuracy: 0.9530, Val Loss: 0.1442, Val Accuracy: 0.9423
Epoch 576/1200, Train Loss: 0.1323, Train Accuracy: 0.9547, Val Loss: 0.1440, Val Accuracy: 0.9421
Epoch 577/1200, Train Loss: 0.1316, Train Accuracy: 0.9553, Val Loss: 0.1438, Val Accuracy: 0.9421
Epoch 578/1200, Train Loss: 0.1315, Train Accuracy: 0.9537, Val Loss: 0.1437, Val Accuracy: 0.9423
Epoch 579/1200, Train Loss: 0.1310, Train Accuracy: 0.9555, Val Loss: 0.1435, Val Accuracy: 0.9417
Epoch 580/1200, Train Loss: 0.1310, Train Accuracy: 0.9550, Val Loss: 0.1434, Val Accuracy: 0.9417
Epoch 581/1200, Train Loss: 0.1301, Train Accuracy: 0.9553, Val Loss: 0.1432, Val Accuracy: 0.9423
Epoch 582/1200, Train Loss: 0.1290, Train Accuracy: 0.9545, Val Loss: 0.1431, Val Accuracy: 0.9423
Epoch 583/1200, Train Loss: 0.1291, Train Accuracy: 0.9564, Val Loss: 0.1429, Val Accuracy: 0.9423
Epoch 584/1200, Train Loss: 0.1286, Train Accuracy: 0.9543, Val Loss: 0.1428, Val Accuracy: 0.9423
Epoch 585/1200, Train Loss: 0.1309, Train Accuracy: 0.9542, Val Loss: 0.1426, Val Accuracy: 0.9421
Epoch 586/1200, Train Loss: 0.1307, Train Accuracy: 0.9554, Val Loss: 0.1425, Val Accuracy: 0.9421
Epoch 587/1200, Train Loss: 0.1298, Train Accuracy: 0.9555, Val Loss: 0.1424, Val Accuracy: 0.9421
Epoch 588/1200, Train Loss: 0.1278, Train Accuracy: 0.9558, Val Loss: 0.1422, Val Accuracy: 0.9421
Epoch 589/1200, Train Loss: 0.1291, Train Accuracy: 0.9563, Val Loss: 0.1421, Val Accuracy: 0.9423
Epoch 590/1200, Train Loss: 0.1293, Train Accuracy: 0.9539, Val Loss: 0.1420, Val Accuracy: 0.9425
Epoch 591/1200, Train Loss: 0.1280, Train Accuracy: 0.9561, Val Loss: 0.1418, Val Accuracy: 0.9423
Epoch 592/1200, Train Loss: 0.1272, Train Accuracy: 0.9565, Val Loss: 0.1417, Val Accuracy: 0.9423
Epoch 593/1200, Train Loss: 0.1268, Train Accuracy: 0.9558, Val Loss: 0.1416, Val Accuracy: 0.9427
Epoch 594/1200, Train Loss: 0.1268, Train Accuracy: 0.9555, Val Loss: 0.1415, Val Accuracy: 0.9428
Epoch 595/1200, Train Loss: 0.1269, Train Accuracy: 0.9557, Val Loss: 0.1414, Val Accuracy: 0.9428
Epoch 596/1200, Train Loss: 0.1260, Train Accuracy: 0.9559, Val Loss: 0.1412, Val Accuracy: 0.9430
Epoch 597/1200, Train Loss: 0.1265, Train Accuracy: 0.9560, Val Loss: 0.1411, Val Accuracy: 0.9427
Epoch 598/1200, Train Loss: 0.1262, Train Accuracy: 0.9556, Val Loss: 0.1409, Val Accuracy: 0.9430
Epoch 599/1200, Train Loss: 0.1268, Train Accuracy: 0.9574, Val Loss: 0.1408, Val Accuracy: 0.9432
Epoch 600/1200, Train Loss: 0.1249, Train Accuracy: 0.9571, Val Loss: 0.1407, Val Accuracy: 0.9436
Epoch 601/1200, Train Loss: 0.1245, Train Accuracy: 0.9576, Val Loss: 0.1406, Val Accuracy: 0.9436
Epoch 602/1200, Train Loss: 0.1244, Train Accuracy: 0.9566, Val Loss: 0.1404, Val Accuracy: 0.9434
Epoch 603/1200, Train Loss: 0.1245, Train Accuracy: 0.9568, Val Loss: 0.1404, Val Accuracy: 0.9442
Epoch 604/1200, Train Loss: 0.1235, Train Accuracy: 0.9578, Val Loss: 0.1402, Val Accuracy: 0.9444
Epoch 605/1200, Train Loss: 0.1243, Train Accuracy: 0.9573, Val Loss: 0.1401, Val Accuracy: 0.9442
Epoch 606/1200, Train Loss: 0.1228, Train Accuracy: 0.9572, Val Loss: 0.1399, Val Accuracy: 0.9442
Epoch 607/1200, Train Loss: 0.1240, Train Accuracy: 0.9562, Val Loss: 0.1398, Val Accuracy: 0.9442
Epoch 608/1200, Train Loss: 0.1228, Train Accuracy: 0.9573, Val Loss: 0.1397, Val Accuracy: 0.9442
Epoch 609/1200, Train Loss: 0.1218, Train Accuracy: 0.9587, Val Loss: 0.1396, Val Accuracy: 0.9444
Epoch 610/1200, Train Loss: 0.1225, Train Accuracy: 0.9579, Val Loss: 0.1395, Val Accuracy: 0.9448
Epoch 611/1200, Train Loss: 0.1220, Train Accuracy: 0.9580, Val Loss: 0.1394, Val Accuracy: 0.9448
Epoch 612/1200, Train Loss: 0.1232, Train Accuracy: 0.9566, Val Loss: 0.1393, Val Accuracy: 0.9448
Epoch 613/1200, Train Loss: 0.1207, Train Accuracy: 0.9587, Val Loss: 0.1391, Val Accuracy: 0.9448
Epoch 614/1200, Train Loss: 0.1194, Train Accuracy: 0.9589, Val Loss: 0.1390, Val Accuracy: 0.9448
Epoch 615/1200, Train Loss: 0.1210, Train Accuracy: 0.9591, Val Loss: 0.1389, Val Accuracy: 0.9452
Epoch 616/1200, Train Loss: 0.1199, Train Accuracy: 0.9593, Val Loss: 0.1388, Val Accuracy: 0.9452
Epoch 617/1200, Train Loss: 0.1200, Train Accuracy: 0.9591, Val Loss: 0.1387, Val Accuracy: 0.9452
Epoch 618/1200, Train Loss: 0.1202, Train Accuracy: 0.9588, Val Loss: 0.1386, Val Accuracy: 0.9456
Epoch 619/1200, Train Loss: 0.1188, Train Accuracy: 0.9586, Val Loss: 0.1384, Val Accuracy: 0.9456
Epoch 620/1200, Train Loss: 0.1171, Train Accuracy: 0.9593, Val Loss: 0.1383, Val Accuracy: 0.9452
Epoch 621/1200, Train Loss: 0.1220, Train Accuracy: 0.9580, Val Loss: 0.1381, Val Accuracy: 0.9456
Epoch 622/1200, Train Loss: 0.1187, Train Accuracy: 0.9595, Val Loss: 0.1380, Val Accuracy: 0.9458
Epoch 623/1200, Train Loss: 0.1180, Train Accuracy: 0.9594, Val Loss: 0.1379, Val Accuracy: 0.9458
Epoch 624/1200, Train Loss: 0.1190, Train Accuracy: 0.9590, Val Loss: 0.1378, Val Accuracy: 0.9458
Epoch 625/1200, Train Loss: 0.1181, Train Accuracy: 0.9591, Val Loss: 0.1377, Val Accuracy: 0.9458
Epoch 626/1200, Train Loss: 0.1175, Train Accuracy: 0.9602, Val Loss: 0.1376, Val Accuracy: 0.9462
Epoch 627/1200, Train Loss: 0.1183, Train Accuracy: 0.9588, Val Loss: 0.1375, Val Accuracy: 0.9463
Epoch 628/1200, Train Loss: 0.1183, Train Accuracy: 0.9598, Val Loss: 0.1374, Val Accuracy: 0.9462
Epoch 629/1200, Train Loss: 0.1177, Train Accuracy: 0.9598, Val Loss: 0.1373, Val Accuracy: 0.9462
Epoch 630/1200, Train Loss: 0.1174, Train Accuracy: 0.9596, Val Loss: 0.1372, Val Accuracy: 0.9462
Epoch 631/1200, Train Loss: 0.1166, Train Accuracy: 0.9587, Val Loss: 0.1371, Val Accuracy: 0.9462
Epoch 632/1200, Train Loss: 0.1171, Train Accuracy: 0.9602, Val Loss: 0.1370, Val Accuracy: 0.9463
Epoch 633/1200, Train Loss: 0.1164, Train Accuracy: 0.9609, Val Loss: 0.1369, Val Accuracy: 0.9465
Epoch 634/1200, Train Loss: 0.1152, Train Accuracy: 0.9594, Val Loss: 0.1369, Val Accuracy: 0.9465
Epoch 635/1200, Train Loss: 0.1155, Train Accuracy: 0.9607, Val Loss: 0.1368, Val Accuracy: 0.9465
Epoch 636/1200, Train Loss: 0.1135, Train Accuracy: 0.9615, Val Loss: 0.1366, Val Accuracy: 0.9467
Epoch 637/1200, Train Loss: 0.1138, Train Accuracy: 0.9618, Val Loss: 0.1365, Val Accuracy: 0.9465
Epoch 638/1200, Train Loss: 0.1145, Train Accuracy: 0.9611, Val Loss: 0.1364, Val Accuracy: 0.9465
Epoch 639/1200, Train Loss: 0.1160, Train Accuracy: 0.9586, Val Loss: 0.1363, Val Accuracy: 0.9465
Epoch 640/1200, Train Loss: 0.1139, Train Accuracy: 0.9620, Val Loss: 0.1363, Val Accuracy: 0.9463
Epoch 641/1200, Train Loss: 0.1128, Train Accuracy: 0.9609, Val Loss: 0.1361, Val Accuracy: 0.9467
Epoch 642/1200, Train Loss: 0.1133, Train Accuracy: 0.9610, Val Loss: 0.1360, Val Accuracy: 0.9469
Epoch 643/1200, Train Loss: 0.1128, Train Accuracy: 0.9614, Val Loss: 0.1359, Val Accuracy: 0.9471
Epoch 644/1200, Train Loss: 0.1138, Train Accuracy: 0.9610, Val Loss: 0.1358, Val Accuracy: 0.9467
Epoch 645/1200, Train Loss: 0.1128, Train Accuracy: 0.9609, Val Loss: 0.1358, Val Accuracy: 0.9473
Epoch 646/1200, Train Loss: 0.1119, Train Accuracy: 0.9623, Val Loss: 0.1357, Val Accuracy: 0.9471
Epoch 647/1200, Train Loss: 0.1134, Train Accuracy: 0.9605, Val Loss: 0.1357, Val Accuracy: 0.9471
Epoch 648/1200, Train Loss: 0.1130, Train Accuracy: 0.9615, Val Loss: 0.1356, Val Accuracy: 0.9473
Epoch 649/1200, Train Loss: 0.1118, Train Accuracy: 0.9617, Val Loss: 0.1355, Val Accuracy: 0.9473
Epoch 650/1200, Train Loss: 0.1124, Train Accuracy: 0.9609, Val Loss: 0.1353, Val Accuracy: 0.9473
Epoch 651/1200, Train Loss: 0.1108, Train Accuracy: 0.9627, Val Loss: 0.1352, Val Accuracy: 0.9473
Epoch 652/1200, Train Loss: 0.1123, Train Accuracy: 0.9620, Val Loss: 0.1351, Val Accuracy: 0.9471
Epoch 653/1200, Train Loss: 0.1112, Train Accuracy: 0.9628, Val Loss: 0.1350, Val Accuracy: 0.9469
Epoch 654/1200, Train Loss: 0.1112, Train Accuracy: 0.9612, Val Loss: 0.1349, Val Accuracy: 0.9467
Epoch 655/1200, Train Loss: 0.1122, Train Accuracy: 0.9618, Val Loss: 0.1348, Val Accuracy: 0.9467
Epoch 656/1200, Train Loss: 0.1101, Train Accuracy: 0.9616, Val Loss: 0.1347, Val Accuracy: 0.9469
Epoch 657/1200, Train Loss: 0.1083, Train Accuracy: 0.9624, Val Loss: 0.1346, Val Accuracy: 0.9469
Epoch 658/1200, Train Loss: 0.1081, Train Accuracy: 0.9649, Val Loss: 0.1346, Val Accuracy: 0.9475
Epoch 659/1200, Train Loss: 0.1091, Train Accuracy: 0.9631, Val Loss: 0.1345, Val Accuracy: 0.9477
Epoch 660/1200, Train Loss: 0.1093, Train Accuracy: 0.9623, Val Loss: 0.1344, Val Accuracy: 0.9475
Epoch 661/1200, Train Loss: 0.1089, Train Accuracy: 0.9629, Val Loss: 0.1344, Val Accuracy: 0.9479
Epoch 662/1200, Train Loss: 0.1099, Train Accuracy: 0.9628, Val Loss: 0.1343, Val Accuracy: 0.9477
Epoch 663/1200, Train Loss: 0.1054, Train Accuracy: 0.9641, Val Loss: 0.1342, Val Accuracy: 0.9477
Epoch 664/1200, Train Loss: 0.1082, Train Accuracy: 0.9617, Val Loss: 0.1340, Val Accuracy: 0.9475
Epoch 665/1200, Train Loss: 0.1069, Train Accuracy: 0.9623, Val Loss: 0.1339, Val Accuracy: 0.9477
Epoch 666/1200, Train Loss: 0.1101, Train Accuracy: 0.9634, Val Loss: 0.1338, Val Accuracy: 0.9479
Epoch 667/1200, Train Loss: 0.1076, Train Accuracy: 0.9625, Val Loss: 0.1338, Val Accuracy: 0.9481
Epoch 668/1200, Train Loss: 0.1090, Train Accuracy: 0.9616, Val Loss: 0.1337, Val Accuracy: 0.9477
Epoch 669/1200, Train Loss: 0.1067, Train Accuracy: 0.9638, Val Loss: 0.1337, Val Accuracy: 0.9475
Epoch 670/1200, Train Loss: 0.1064, Train Accuracy: 0.9646, Val Loss: 0.1335, Val Accuracy: 0.9483
Epoch 671/1200, Train Loss: 0.1068, Train Accuracy: 0.9631, Val Loss: 0.1334, Val Accuracy: 0.9483
Epoch 672/1200, Train Loss: 0.1060, Train Accuracy: 0.9637, Val Loss: 0.1334, Val Accuracy: 0.9483
Epoch 673/1200, Train Loss: 0.1074, Train Accuracy: 0.9637, Val Loss: 0.1334, Val Accuracy: 0.9483
Epoch 674/1200, Train Loss: 0.1050, Train Accuracy: 0.9644, Val Loss: 0.1333, Val Accuracy: 0.9483
Epoch 675/1200, Train Loss: 0.1052, Train Accuracy: 0.9654, Val Loss: 0.1332, Val Accuracy: 0.9485
Epoch 676/1200, Train Loss: 0.1043, Train Accuracy: 0.9634, Val Loss: 0.1332, Val Accuracy: 0.9483
Epoch 677/1200, Train Loss: 0.1071, Train Accuracy: 0.9626, Val Loss: 0.1332, Val Accuracy: 0.9479
Epoch 678/1200, Train Loss: 0.1072, Train Accuracy: 0.9644, Val Loss: 0.1331, Val Accuracy: 0.9481
Epoch 679/1200, Train Loss: 0.1052, Train Accuracy: 0.9638, Val Loss: 0.1330, Val Accuracy: 0.9481
Epoch 680/1200, Train Loss: 0.1052, Train Accuracy: 0.9652, Val Loss: 0.1329, Val Accuracy: 0.9479
Epoch 681/1200, Train Loss: 0.1046, Train Accuracy: 0.9642, Val Loss: 0.1328, Val Accuracy: 0.9485
Epoch 682/1200, Train Loss: 0.1051, Train Accuracy: 0.9639, Val Loss: 0.1327, Val Accuracy: 0.9485
Epoch 683/1200, Train Loss: 0.1024, Train Accuracy: 0.9653, Val Loss: 0.1326, Val Accuracy: 0.9485
Epoch 684/1200, Train Loss: 0.1035, Train Accuracy: 0.9653, Val Loss: 0.1326, Val Accuracy: 0.9483
Epoch 685/1200, Train Loss: 0.1023, Train Accuracy: 0.9650, Val Loss: 0.1325, Val Accuracy: 0.9481
Epoch 686/1200, Train Loss: 0.1058, Train Accuracy: 0.9646, Val Loss: 0.1325, Val Accuracy: 0.9483
Epoch 687/1200, Train Loss: 0.1040, Train Accuracy: 0.9650, Val Loss: 0.1324, Val Accuracy: 0.9479
Epoch 688/1200, Train Loss: 0.1036, Train Accuracy: 0.9652, Val Loss: 0.1323, Val Accuracy: 0.9483
Epoch 689/1200, Train Loss: 0.1021, Train Accuracy: 0.9662, Val Loss: 0.1322, Val Accuracy: 0.9483
Epoch 690/1200, Train Loss: 0.1024, Train Accuracy: 0.9661, Val Loss: 0.1322, Val Accuracy: 0.9483
Epoch 691/1200, Train Loss: 0.1030, Train Accuracy: 0.9657, Val Loss: 0.1321, Val Accuracy: 0.9483
Epoch 692/1200, Train Loss: 0.1024, Train Accuracy: 0.9656, Val Loss: 0.1321, Val Accuracy: 0.9483
Epoch 693/1200, Train Loss: 0.1013, Train Accuracy: 0.9659, Val Loss: 0.1320, Val Accuracy: 0.9483
Epoch 694/1200, Train Loss: 0.1025, Train Accuracy: 0.9660, Val Loss: 0.1320, Val Accuracy: 0.9479
Epoch 695/1200, Train Loss: 0.1020, Train Accuracy: 0.9659, Val Loss: 0.1319, Val Accuracy: 0.9477
Epoch 696/1200, Train Loss: 0.0986, Train Accuracy: 0.9665, Val Loss: 0.1318, Val Accuracy: 0.9481
Epoch 697/1200, Train Loss: 0.1011, Train Accuracy: 0.9662, Val Loss: 0.1318, Val Accuracy: 0.9481
Epoch 698/1200, Train Loss: 0.1014, Train Accuracy: 0.9647, Val Loss: 0.1317, Val Accuracy: 0.9477
Epoch 699/1200, Train Loss: 0.1012, Train Accuracy: 0.9663, Val Loss: 0.1316, Val Accuracy: 0.9483
Epoch 700/1200, Train Loss: 0.0997, Train Accuracy: 0.9670, Val Loss: 0.1316, Val Accuracy: 0.9481
Epoch 701/1200, Train Loss: 0.1007, Train Accuracy: 0.9676, Val Loss: 0.1315, Val Accuracy: 0.9481
Epoch 702/1200, Train Loss: 0.1009, Train Accuracy: 0.9663, Val Loss: 0.1314, Val Accuracy: 0.9479
Epoch 703/1200, Train Loss: 0.0994, Train Accuracy: 0.9667, Val Loss: 0.1313, Val Accuracy: 0.9477
Epoch 704/1200, Train Loss: 0.1002, Train Accuracy: 0.9664, Val Loss: 0.1313, Val Accuracy: 0.9479
Epoch 705/1200, Train Loss: 0.1004, Train Accuracy: 0.9658, Val Loss: 0.1312, Val Accuracy: 0.9481
Epoch 706/1200, Train Loss: 0.0979, Train Accuracy: 0.9674, Val Loss: 0.1311, Val Accuracy: 0.9481
Epoch 707/1200, Train Loss: 0.0988, Train Accuracy: 0.9666, Val Loss: 0.1310, Val Accuracy: 0.9485
Epoch 708/1200, Train Loss: 0.0993, Train Accuracy: 0.9669, Val Loss: 0.1310, Val Accuracy: 0.9483
Epoch 709/1200, Train Loss: 0.0984, Train Accuracy: 0.9673, Val Loss: 0.1309, Val Accuracy: 0.9483
Epoch 710/1200, Train Loss: 0.0974, Train Accuracy: 0.9686, Val Loss: 0.1309, Val Accuracy: 0.9485
Epoch 711/1200, Train Loss: 0.1002, Train Accuracy: 0.9663, Val Loss: 0.1309, Val Accuracy: 0.9481
Epoch 712/1200, Train Loss: 0.0985, Train Accuracy: 0.9673, Val Loss: 0.1308, Val Accuracy: 0.9483
Epoch 713/1200, Train Loss: 0.0965, Train Accuracy: 0.9686, Val Loss: 0.1307, Val Accuracy: 0.9483
Epoch 714/1200, Train Loss: 0.0964, Train Accuracy: 0.9686, Val Loss: 0.1306, Val Accuracy: 0.9481
Epoch 715/1200, Train Loss: 0.0970, Train Accuracy: 0.9675, Val Loss: 0.1306, Val Accuracy: 0.9481
Epoch 716/1200, Train Loss: 0.0968, Train Accuracy: 0.9672, Val Loss: 0.1305, Val Accuracy: 0.9483
Epoch 717/1200, Train Loss: 0.0978, Train Accuracy: 0.9664, Val Loss: 0.1304, Val Accuracy: 0.9483
Epoch 718/1200, Train Loss: 0.0976, Train Accuracy: 0.9678, Val Loss: 0.1304, Val Accuracy: 0.9483
Epoch 719/1200, Train Loss: 0.0951, Train Accuracy: 0.9694, Val Loss: 0.1304, Val Accuracy: 0.9483
Epoch 720/1200, Train Loss: 0.0971, Train Accuracy: 0.9666, Val Loss: 0.1303, Val Accuracy: 0.9483
Epoch 721/1200, Train Loss: 0.0965, Train Accuracy: 0.9670, Val Loss: 0.1303, Val Accuracy: 0.9483
Epoch 722/1200, Train Loss: 0.0944, Train Accuracy: 0.9691, Val Loss: 0.1303, Val Accuracy: 0.9483
Epoch 723/1200, Train Loss: 0.0961, Train Accuracy: 0.9677, Val Loss: 0.1302, Val Accuracy: 0.9483
Epoch 724/1200, Train Loss: 0.0959, Train Accuracy: 0.9669, Val Loss: 0.1302, Val Accuracy: 0.9481
Epoch 725/1200, Train Loss: 0.0942, Train Accuracy: 0.9683, Val Loss: 0.1301, Val Accuracy: 0.9481
Epoch 726/1200, Train Loss: 0.0942, Train Accuracy: 0.9686, Val Loss: 0.1300, Val Accuracy: 0.9481
Epoch 727/1200, Train Loss: 0.0972, Train Accuracy: 0.9674, Val Loss: 0.1300, Val Accuracy: 0.9479
Epoch 728/1200, Train Loss: 0.0954, Train Accuracy: 0.9687, Val Loss: 0.1299, Val Accuracy: 0.9479
Epoch 729/1200, Train Loss: 0.0929, Train Accuracy: 0.9699, Val Loss: 0.1299, Val Accuracy: 0.9481
Epoch 730/1200, Train Loss: 0.0952, Train Accuracy: 0.9680, Val Loss: 0.1298, Val Accuracy: 0.9483
Epoch 731/1200, Train Loss: 0.0935, Train Accuracy: 0.9687, Val Loss: 0.1298, Val Accuracy: 0.9481
Epoch 732/1200, Train Loss: 0.0942, Train Accuracy: 0.9684, Val Loss: 0.1298, Val Accuracy: 0.9477
Epoch 733/1200, Train Loss: 0.0931, Train Accuracy: 0.9680, Val Loss: 0.1297, Val Accuracy: 0.9477
Epoch 734/1200, Train Loss: 0.0918, Train Accuracy: 0.9697, Val Loss: 0.1296, Val Accuracy: 0.9481
Epoch 735/1200, Train Loss: 0.0936, Train Accuracy: 0.9700, Val Loss: 0.1295, Val Accuracy: 0.9479
Epoch 736/1200, Train Loss: 0.0934, Train Accuracy: 0.9695, Val Loss: 0.1294, Val Accuracy: 0.9479
Epoch 737/1200, Train Loss: 0.0924, Train Accuracy: 0.9685, Val Loss: 0.1294, Val Accuracy: 0.9479
Epoch 738/1200, Train Loss: 0.0915, Train Accuracy: 0.9689, Val Loss: 0.1294, Val Accuracy: 0.9479
Epoch 739/1200, Train Loss: 0.0918, Train Accuracy: 0.9700, Val Loss: 0.1293, Val Accuracy: 0.9479
Epoch 740/1200, Train Loss: 0.0936, Train Accuracy: 0.9686, Val Loss: 0.1292, Val Accuracy: 0.9479
Epoch 741/1200, Train Loss: 0.0935, Train Accuracy: 0.9690, Val Loss: 0.1291, Val Accuracy: 0.9477
Epoch 742/1200, Train Loss: 0.0916, Train Accuracy: 0.9690, Val Loss: 0.1291, Val Accuracy: 0.9477
Epoch 743/1200, Train Loss: 0.0920, Train Accuracy: 0.9701, Val Loss: 0.1291, Val Accuracy: 0.9481
Epoch 744/1200, Train Loss: 0.0908, Train Accuracy: 0.9703, Val Loss: 0.1290, Val Accuracy: 0.9481
Epoch 745/1200, Train Loss: 0.0907, Train Accuracy: 0.9709, Val Loss: 0.1290, Val Accuracy: 0.9479
Epoch 746/1200, Train Loss: 0.0893, Train Accuracy: 0.9713, Val Loss: 0.1289, Val Accuracy: 0.9481
Epoch 747/1200, Train Loss: 0.0903, Train Accuracy: 0.9702, Val Loss: 0.1289, Val Accuracy: 0.9481
Epoch 748/1200, Train Loss: 0.0897, Train Accuracy: 0.9712, Val Loss: 0.1289, Val Accuracy: 0.9481
Epoch 749/1200, Train Loss: 0.0917, Train Accuracy: 0.9690, Val Loss: 0.1288, Val Accuracy: 0.9481
Epoch 750/1200, Train Loss: 0.0929, Train Accuracy: 0.9699, Val Loss: 0.1288, Val Accuracy: 0.9479
Epoch 751/1200, Train Loss: 0.0912, Train Accuracy: 0.9693, Val Loss: 0.1287, Val Accuracy: 0.9479
Epoch 752/1200, Train Loss: 0.0897, Train Accuracy: 0.9709, Val Loss: 0.1287, Val Accuracy: 0.9479
Epoch 753/1200, Train Loss: 0.0883, Train Accuracy: 0.9723, Val Loss: 0.1287, Val Accuracy: 0.9479
Epoch 754/1200, Train Loss: 0.0894, Train Accuracy: 0.9704, Val Loss: 0.1286, Val Accuracy: 0.9481
Epoch 755/1200, Train Loss: 0.0889, Train Accuracy: 0.9709, Val Loss: 0.1286, Val Accuracy: 0.9479
Epoch 756/1200, Train Loss: 0.0893, Train Accuracy: 0.9704, Val Loss: 0.1286, Val Accuracy: 0.9481
Epoch 757/1200, Train Loss: 0.0886, Train Accuracy: 0.9697, Val Loss: 0.1286, Val Accuracy: 0.9479
Epoch 758/1200, Train Loss: 0.0876, Train Accuracy: 0.9713, Val Loss: 0.1286, Val Accuracy: 0.9481
Epoch 759/1200, Train Loss: 0.0901, Train Accuracy: 0.9710, Val Loss: 0.1286, Val Accuracy: 0.9481
Epoch 760/1200, Train Loss: 0.0872, Train Accuracy: 0.9721, Val Loss: 0.1285, Val Accuracy: 0.9479
Epoch 761/1200, Train Loss: 0.0876, Train Accuracy: 0.9715, Val Loss: 0.1284, Val Accuracy: 0.9481
Epoch 762/1200, Train Loss: 0.0875, Train Accuracy: 0.9716, Val Loss: 0.1284, Val Accuracy: 0.9481
Epoch 763/1200, Train Loss: 0.0869, Train Accuracy: 0.9714, Val Loss: 0.1283, Val Accuracy: 0.9479
Epoch 764/1200, Train Loss: 0.0866, Train Accuracy: 0.9712, Val Loss: 0.1283, Val Accuracy: 0.9481
Epoch 765/1200, Train Loss: 0.0895, Train Accuracy: 0.9705, Val Loss: 0.1282, Val Accuracy: 0.9479
Epoch 766/1200, Train Loss: 0.0864, Train Accuracy: 0.9719, Val Loss: 0.1282, Val Accuracy: 0.9481
Epoch 767/1200, Train Loss: 0.0885, Train Accuracy: 0.9698, Val Loss: 0.1281, Val Accuracy: 0.9477
Epoch 768/1200, Train Loss: 0.0865, Train Accuracy: 0.9721, Val Loss: 0.1281, Val Accuracy: 0.9479
Epoch 769/1200, Train Loss: 0.0857, Train Accuracy: 0.9724, Val Loss: 0.1281, Val Accuracy: 0.9479
Epoch 770/1200, Train Loss: 0.0858, Train Accuracy: 0.9708, Val Loss: 0.1281, Val Accuracy: 0.9481
Epoch 771/1200, Train Loss: 0.0866, Train Accuracy: 0.9718, Val Loss: 0.1281, Val Accuracy: 0.9483
Epoch 772/1200, Train Loss: 0.0863, Train Accuracy: 0.9716, Val Loss: 0.1279, Val Accuracy: 0.9481
Epoch 773/1200, Train Loss: 0.0851, Train Accuracy: 0.9720, Val Loss: 0.1279, Val Accuracy: 0.9481
Epoch 774/1200, Train Loss: 0.0835, Train Accuracy: 0.9736, Val Loss: 0.1278, Val Accuracy: 0.9479
Epoch 775/1200, Train Loss: 0.0857, Train Accuracy: 0.9714, Val Loss: 0.1279, Val Accuracy: 0.9477
Epoch 776/1200, Train Loss: 0.0850, Train Accuracy: 0.9727, Val Loss: 0.1279, Val Accuracy: 0.9479
Epoch 777/1200, Train Loss: 0.0849, Train Accuracy: 0.9725, Val Loss: 0.1278, Val Accuracy: 0.9481
Epoch 778/1200, Train Loss: 0.0848, Train Accuracy: 0.9715, Val Loss: 0.1277, Val Accuracy: 0.9483
Epoch 779/1200, Train Loss: 0.0849, Train Accuracy: 0.9723, Val Loss: 0.1277, Val Accuracy: 0.9483
Epoch 780/1200, Train Loss: 0.0846, Train Accuracy: 0.9734, Val Loss: 0.1276, Val Accuracy: 0.9481
Epoch 781/1200, Train Loss: 0.0867, Train Accuracy: 0.9713, Val Loss: 0.1277, Val Accuracy: 0.9481
Epoch 782/1200, Train Loss: 0.0834, Train Accuracy: 0.9729, Val Loss: 0.1277, Val Accuracy: 0.9487
Epoch 783/1200, Train Loss: 0.0827, Train Accuracy: 0.9729, Val Loss: 0.1277, Val Accuracy: 0.9487
Epoch 784/1200, Train Loss: 0.0825, Train Accuracy: 0.9733, Val Loss: 0.1277, Val Accuracy: 0.9487
Epoch 785/1200, Train Loss: 0.0841, Train Accuracy: 0.9726, Val Loss: 0.1277, Val Accuracy: 0.9487
Epoch 786/1200, Train Loss: 0.0824, Train Accuracy: 0.9737, Val Loss: 0.1277, Val Accuracy: 0.9483
Epoch 787/1200, Train Loss: 0.0837, Train Accuracy: 0.9739, Val Loss: 0.1275, Val Accuracy: 0.9489
Epoch 788/1200, Train Loss: 0.0829, Train Accuracy: 0.9739, Val Loss: 0.1274, Val Accuracy: 0.9485
Epoch 789/1200, Train Loss: 0.0811, Train Accuracy: 0.9742, Val Loss: 0.1274, Val Accuracy: 0.9487
Epoch 790/1200, Train Loss: 0.0838, Train Accuracy: 0.9726, Val Loss: 0.1274, Val Accuracy: 0.9485
Epoch 791/1200, Train Loss: 0.0831, Train Accuracy: 0.9739, Val Loss: 0.1273, Val Accuracy: 0.9487
Epoch 792/1200, Train Loss: 0.0831, Train Accuracy: 0.9731, Val Loss: 0.1273, Val Accuracy: 0.9485
Epoch 793/1200, Train Loss: 0.0825, Train Accuracy: 0.9730, Val Loss: 0.1273, Val Accuracy: 0.9487
Epoch 794/1200, Train Loss: 0.0821, Train Accuracy: 0.9736, Val Loss: 0.1272, Val Accuracy: 0.9487
Epoch 795/1200, Train Loss: 0.0813, Train Accuracy: 0.9736, Val Loss: 0.1272, Val Accuracy: 0.9487
Epoch 796/1200, Train Loss: 0.0804, Train Accuracy: 0.9741, Val Loss: 0.1272, Val Accuracy: 0.9487
Epoch 797/1200, Train Loss: 0.0830, Train Accuracy: 0.9729, Val Loss: 0.1271, Val Accuracy: 0.9487
Epoch 798/1200, Train Loss: 0.0806, Train Accuracy: 0.9728, Val Loss: 0.1272, Val Accuracy: 0.9489
Epoch 799/1200, Train Loss: 0.0801, Train Accuracy: 0.9740, Val Loss: 0.1272, Val Accuracy: 0.9487
Epoch 800/1200, Train Loss: 0.0811, Train Accuracy: 0.9742, Val Loss: 0.1272, Val Accuracy: 0.9487
Epoch 801/1200, Train Loss: 0.0819, Train Accuracy: 0.9741, Val Loss: 0.1272, Val Accuracy: 0.9485
Epoch 802/1200, Train Loss: 0.0812, Train Accuracy: 0.9736, Val Loss: 0.1271, Val Accuracy: 0.9487
Epoch 803/1200, Train Loss: 0.0808, Train Accuracy: 0.9741, Val Loss: 0.1271, Val Accuracy: 0.9487
Epoch 804/1200, Train Loss: 0.0800, Train Accuracy: 0.9743, Val Loss: 0.1270, Val Accuracy: 0.9487
Epoch 805/1200, Train Loss: 0.0785, Train Accuracy: 0.9755, Val Loss: 0.1271, Val Accuracy: 0.9487
Epoch 806/1200, Train Loss: 0.0779, Train Accuracy: 0.9749, Val Loss: 0.1272, Val Accuracy: 0.9487
Epoch 807/1200, Train Loss: 0.0802, Train Accuracy: 0.9743, Val Loss: 0.1271, Val Accuracy: 0.9485
Epoch 808/1200, Train Loss: 0.0812, Train Accuracy: 0.9725, Val Loss: 0.1270, Val Accuracy: 0.9485
Epoch 809/1200, Train Loss: 0.0777, Train Accuracy: 0.9756, Val Loss: 0.1271, Val Accuracy: 0.9487
Epoch 810/1200, Train Loss: 0.0791, Train Accuracy: 0.9752, Val Loss: 0.1270, Val Accuracy: 0.9485
Epoch 811/1200, Train Loss: 0.0800, Train Accuracy: 0.9741, Val Loss: 0.1268, Val Accuracy: 0.9491
Epoch 812/1200, Train Loss: 0.0792, Train Accuracy: 0.9743, Val Loss: 0.1268, Val Accuracy: 0.9493
Epoch 813/1200, Train Loss: 0.0791, Train Accuracy: 0.9737, Val Loss: 0.1267, Val Accuracy: 0.9493
Epoch 814/1200, Train Loss: 0.0786, Train Accuracy: 0.9743, Val Loss: 0.1267, Val Accuracy: 0.9493
Epoch 815/1200, Train Loss: 0.0779, Train Accuracy: 0.9747, Val Loss: 0.1267, Val Accuracy: 0.9491
Epoch 816/1200, Train Loss: 0.0777, Train Accuracy: 0.9749, Val Loss: 0.1266, Val Accuracy: 0.9493
Epoch 817/1200, Train Loss: 0.0789, Train Accuracy: 0.9745, Val Loss: 0.1266, Val Accuracy: 0.9493
Epoch 818/1200, Train Loss: 0.0780, Train Accuracy: 0.9755, Val Loss: 0.1266, Val Accuracy: 0.9493
Epoch 819/1200, Train Loss: 0.0790, Train Accuracy: 0.9738, Val Loss: 0.1267, Val Accuracy: 0.9491
Epoch 820/1200, Train Loss: 0.0787, Train Accuracy: 0.9748, Val Loss: 0.1266, Val Accuracy: 0.9491
Epoch 821/1200, Train Loss: 0.0761, Train Accuracy: 0.9765, Val Loss: 0.1266, Val Accuracy: 0.9493
Epoch 822/1200, Train Loss: 0.0778, Train Accuracy: 0.9752, Val Loss: 0.1265, Val Accuracy: 0.9493
Epoch 823/1200, Train Loss: 0.0772, Train Accuracy: 0.9753, Val Loss: 0.1265, Val Accuracy: 0.9493
Epoch 824/1200, Train Loss: 0.0769, Train Accuracy: 0.9760, Val Loss: 0.1264, Val Accuracy: 0.9491
Epoch 825/1200, Train Loss: 0.0761, Train Accuracy: 0.9762, Val Loss: 0.1264, Val Accuracy: 0.9493
Epoch 826/1200, Train Loss: 0.0755, Train Accuracy: 0.9759, Val Loss: 0.1263, Val Accuracy: 0.9493
Epoch 827/1200, Train Loss: 0.0758, Train Accuracy: 0.9758, Val Loss: 0.1263, Val Accuracy: 0.9495
Epoch 828/1200, Train Loss: 0.0773, Train Accuracy: 0.9753, Val Loss: 0.1263, Val Accuracy: 0.9493
Epoch 829/1200, Train Loss: 0.0758, Train Accuracy: 0.9761, Val Loss: 0.1262, Val Accuracy: 0.9497
Epoch 830/1200, Train Loss: 0.0751, Train Accuracy: 0.9756, Val Loss: 0.1261, Val Accuracy: 0.9498
Epoch 831/1200, Train Loss: 0.0759, Train Accuracy: 0.9753, Val Loss: 0.1261, Val Accuracy: 0.9498
Epoch 832/1200, Train Loss: 0.0750, Train Accuracy: 0.9760, Val Loss: 0.1261, Val Accuracy: 0.9498
Epoch 833/1200, Train Loss: 0.0752, Train Accuracy: 0.9759, Val Loss: 0.1260, Val Accuracy: 0.9500
Epoch 834/1200, Train Loss: 0.0754, Train Accuracy: 0.9765, Val Loss: 0.1259, Val Accuracy: 0.9502
Epoch 835/1200, Train Loss: 0.0752, Train Accuracy: 0.9762, Val Loss: 0.1260, Val Accuracy: 0.9498
Epoch 836/1200, Train Loss: 0.0746, Train Accuracy: 0.9759, Val Loss: 0.1261, Val Accuracy: 0.9500
Epoch 837/1200, Train Loss: 0.0744, Train Accuracy: 0.9763, Val Loss: 0.1260, Val Accuracy: 0.9500
Epoch 838/1200, Train Loss: 0.0732, Train Accuracy: 0.9762, Val Loss: 0.1260, Val Accuracy: 0.9502
Epoch 839/1200, Train Loss: 0.0746, Train Accuracy: 0.9761, Val Loss: 0.1261, Val Accuracy: 0.9500
Epoch 840/1200, Train Loss: 0.0725, Train Accuracy: 0.9772, Val Loss: 0.1260, Val Accuracy: 0.9500
Epoch 841/1200, Train Loss: 0.0733, Train Accuracy: 0.9759, Val Loss: 0.1261, Val Accuracy: 0.9498
Epoch 842/1200, Train Loss: 0.0728, Train Accuracy: 0.9773, Val Loss: 0.1261, Val Accuracy: 0.9498
Epoch 843/1200, Train Loss: 0.0734, Train Accuracy: 0.9772, Val Loss: 0.1260, Val Accuracy: 0.9500
Epoch 844/1200, Train Loss: 0.0734, Train Accuracy: 0.9771, Val Loss: 0.1259, Val Accuracy: 0.9500
Epoch 845/1200, Train Loss: 0.0712, Train Accuracy: 0.9785, Val Loss: 0.1258, Val Accuracy: 0.9498
Epoch 846/1200, Train Loss: 0.0736, Train Accuracy: 0.9772, Val Loss: 0.1259, Val Accuracy: 0.9500
Epoch 847/1200, Train Loss: 0.0724, Train Accuracy: 0.9766, Val Loss: 0.1258, Val Accuracy: 0.9497
Epoch 848/1200, Train Loss: 0.0718, Train Accuracy: 0.9773, Val Loss: 0.1259, Val Accuracy: 0.9500
Epoch 849/1200, Train Loss: 0.0714, Train Accuracy: 0.9777, Val Loss: 0.1260, Val Accuracy: 0.9498
Epoch 850/1200, Train Loss: 0.0712, Train Accuracy: 0.9781, Val Loss: 0.1260, Val Accuracy: 0.9498
Epoch 851/1200, Train Loss: 0.0722, Train Accuracy: 0.9772, Val Loss: 0.1260, Val Accuracy: 0.9498
Epoch 852/1200, Train Loss: 0.0706, Train Accuracy: 0.9782, Val Loss: 0.1261, Val Accuracy: 0.9500
Epoch 853/1200, Train Loss: 0.0714, Train Accuracy: 0.9788, Val Loss: 0.1261, Val Accuracy: 0.9502
Epoch 854/1200, Train Loss: 0.0723, Train Accuracy: 0.9770, Val Loss: 0.1258, Val Accuracy: 0.9502
Epoch 855/1200, Train Loss: 0.0715, Train Accuracy: 0.9779, Val Loss: 0.1258, Val Accuracy: 0.9498
Epoch 856/1200, Train Loss: 0.0719, Train Accuracy: 0.9778, Val Loss: 0.1259, Val Accuracy: 0.9500
Epoch 857/1200, Train Loss: 0.0708, Train Accuracy: 0.9776, Val Loss: 0.1258, Val Accuracy: 0.9500
Epoch 858/1200, Train Loss: 0.0721, Train Accuracy: 0.9762, Val Loss: 0.1256, Val Accuracy: 0.9502
Epoch 859/1200, Train Loss: 0.0709, Train Accuracy: 0.9780, Val Loss: 0.1256, Val Accuracy: 0.9502
Epoch 860/1200, Train Loss: 0.0691, Train Accuracy: 0.9786, Val Loss: 0.1256, Val Accuracy: 0.9502
Epoch 861/1200, Train Loss: 0.0698, Train Accuracy: 0.9776, Val Loss: 0.1256, Val Accuracy: 0.9502
Epoch 862/1200, Train Loss: 0.0695, Train Accuracy: 0.9781, Val Loss: 0.1257, Val Accuracy: 0.9502
Epoch 863/1200, Train Loss: 0.0707, Train Accuracy: 0.9781, Val Loss: 0.1257, Val Accuracy: 0.9504
Epoch 864/1200, Train Loss: 0.0699, Train Accuracy: 0.9783, Val Loss: 0.1256, Val Accuracy: 0.9502
Epoch 865/1200, Train Loss: 0.0696, Train Accuracy: 0.9781, Val Loss: 0.1256, Val Accuracy: 0.9502
Epoch 866/1200, Train Loss: 0.0710, Train Accuracy: 0.9780, Val Loss: 0.1256, Val Accuracy: 0.9504
Epoch 867/1200, Train Loss: 0.0713, Train Accuracy: 0.9764, Val Loss: 0.1256, Val Accuracy: 0.9508
Epoch 868/1200, Train Loss: 0.0688, Train Accuracy: 0.9790, Val Loss: 0.1257, Val Accuracy: 0.9502
Epoch 869/1200, Train Loss: 0.0685, Train Accuracy: 0.9792, Val Loss: 0.1256, Val Accuracy: 0.9502
Epoch 870/1200, Train Loss: 0.0694, Train Accuracy: 0.9780, Val Loss: 0.1255, Val Accuracy: 0.9504
Epoch 871/1200, Train Loss: 0.0701, Train Accuracy: 0.9784, Val Loss: 0.1256, Val Accuracy: 0.9504
Epoch 872/1200, Train Loss: 0.0670, Train Accuracy: 0.9807, Val Loss: 0.1256, Val Accuracy: 0.9502
Epoch 873/1200, Train Loss: 0.0681, Train Accuracy: 0.9789, Val Loss: 0.1258, Val Accuracy: 0.9508
Epoch 874/1200, Train Loss: 0.0679, Train Accuracy: 0.9784, Val Loss: 0.1256, Val Accuracy: 0.9502
Epoch 875/1200, Train Loss: 0.0690, Train Accuracy: 0.9782, Val Loss: 0.1257, Val Accuracy: 0.9506
Epoch 876/1200, Train Loss: 0.0675, Train Accuracy: 0.9790, Val Loss: 0.1255, Val Accuracy: 0.9504
Epoch 877/1200, Train Loss: 0.0681, Train Accuracy: 0.9783, Val Loss: 0.1257, Val Accuracy: 0.9506
Epoch 878/1200, Train Loss: 0.0686, Train Accuracy: 0.9792, Val Loss: 0.1256, Val Accuracy: 0.9508
Epoch 879/1200, Train Loss: 0.0683, Train Accuracy: 0.9786, Val Loss: 0.1257, Val Accuracy: 0.9506
Epoch 880/1200, Train Loss: 0.0666, Train Accuracy: 0.9806, Val Loss: 0.1257, Val Accuracy: 0.9508
Epoch 881/1200, Train Loss: 0.0681, Train Accuracy: 0.9787, Val Loss: 0.1255, Val Accuracy: 0.9510
Epoch 882/1200, Train Loss: 0.0667, Train Accuracy: 0.9799, Val Loss: 0.1255, Val Accuracy: 0.9508
Epoch 883/1200, Train Loss: 0.0661, Train Accuracy: 0.9792, Val Loss: 0.1255, Val Accuracy: 0.9508
Epoch 884/1200, Train Loss: 0.0680, Train Accuracy: 0.9793, Val Loss: 0.1254, Val Accuracy: 0.9510
Epoch 885/1200, Train Loss: 0.0658, Train Accuracy: 0.9793, Val Loss: 0.1255, Val Accuracy: 0.9506
Epoch 886/1200, Train Loss: 0.0681, Train Accuracy: 0.9793, Val Loss: 0.1254, Val Accuracy: 0.9510
Epoch 887/1200, Train Loss: 0.0673, Train Accuracy: 0.9794, Val Loss: 0.1253, Val Accuracy: 0.9510
Epoch 888/1200, Train Loss: 0.0641, Train Accuracy: 0.9805, Val Loss: 0.1254, Val Accuracy: 0.9508
Epoch 889/1200, Train Loss: 0.0681, Train Accuracy: 0.9788, Val Loss: 0.1253, Val Accuracy: 0.9510
Epoch 890/1200, Train Loss: 0.0660, Train Accuracy: 0.9797, Val Loss: 0.1254, Val Accuracy: 0.9514
Epoch 891/1200, Train Loss: 0.0656, Train Accuracy: 0.9802, Val Loss: 0.1254, Val Accuracy: 0.9512
Epoch 892/1200, Train Loss: 0.0654, Train Accuracy: 0.9789, Val Loss: 0.1252, Val Accuracy: 0.9508
Epoch 893/1200, Train Loss: 0.0661, Train Accuracy: 0.9796, Val Loss: 0.1252, Val Accuracy: 0.9508
Epoch 894/1200, Train Loss: 0.0650, Train Accuracy: 0.9800, Val Loss: 0.1253, Val Accuracy: 0.9508
Epoch 895/1200, Train Loss: 0.0649, Train Accuracy: 0.9803, Val Loss: 0.1253, Val Accuracy: 0.9510
Epoch 896/1200, Train Loss: 0.0642, Train Accuracy: 0.9811, Val Loss: 0.1253, Val Accuracy: 0.9510
Epoch 897/1200, Train Loss: 0.0649, Train Accuracy: 0.9795, Val Loss: 0.1253, Val Accuracy: 0.9510
Epoch 898/1200, Train Loss: 0.0653, Train Accuracy: 0.9797, Val Loss: 0.1253, Val Accuracy: 0.9510
Epoch 899/1200, Train Loss: 0.0652, Train Accuracy: 0.9791, Val Loss: 0.1252, Val Accuracy: 0.9510
Epoch 900/1200, Train Loss: 0.0646, Train Accuracy: 0.9799, Val Loss: 0.1252, Val Accuracy: 0.9510
Epoch 901/1200, Train Loss: 0.0643, Train Accuracy: 0.9802, Val Loss: 0.1252, Val Accuracy: 0.9510
Epoch 902/1200, Train Loss: 0.0638, Train Accuracy: 0.9806, Val Loss: 0.1252, Val Accuracy: 0.9514
Epoch 903/1200, Train Loss: 0.0632, Train Accuracy: 0.9804, Val Loss: 0.1251, Val Accuracy: 0.9510
Epoch 904/1200, Train Loss: 0.0633, Train Accuracy: 0.9815, Val Loss: 0.1251, Val Accuracy: 0.9510
Epoch 905/1200, Train Loss: 0.0631, Train Accuracy: 0.9801, Val Loss: 0.1253, Val Accuracy: 0.9512
Epoch 906/1200, Train Loss: 0.0645, Train Accuracy: 0.9793, Val Loss: 0.1254, Val Accuracy: 0.9514
Epoch 907/1200, Train Loss: 0.0620, Train Accuracy: 0.9816, Val Loss: 0.1252, Val Accuracy: 0.9516
Epoch 908/1200, Train Loss: 0.0645, Train Accuracy: 0.9813, Val Loss: 0.1252, Val Accuracy: 0.9516
Epoch 909/1200, Train Loss: 0.0631, Train Accuracy: 0.9807, Val Loss: 0.1252, Val Accuracy: 0.9516
Epoch 910/1200, Train Loss: 0.0631, Train Accuracy: 0.9817, Val Loss: 0.1252, Val Accuracy: 0.9514
Epoch 911/1200, Train Loss: 0.0633, Train Accuracy: 0.9809, Val Loss: 0.1252, Val Accuracy: 0.9514
Epoch 912/1200, Train Loss: 0.0633, Train Accuracy: 0.9808, Val Loss: 0.1252, Val Accuracy: 0.9514
Epoch 913/1200, Train Loss: 0.0614, Train Accuracy: 0.9812, Val Loss: 0.1252, Val Accuracy: 0.9514
Epoch 914/1200, Train Loss: 0.0627, Train Accuracy: 0.9806, Val Loss: 0.1254, Val Accuracy: 0.9514
Epoch 915/1200, Train Loss: 0.0620, Train Accuracy: 0.9808, Val Loss: 0.1252, Val Accuracy: 0.9512
Epoch 916/1200, Train Loss: 0.0621, Train Accuracy: 0.9808, Val Loss: 0.1253, Val Accuracy: 0.9512
Epoch 917/1200, Train Loss: 0.0609, Train Accuracy: 0.9818, Val Loss: 0.1253, Val Accuracy: 0.9516
Epoch 918/1200, Train Loss: 0.0635, Train Accuracy: 0.9808, Val Loss: 0.1252, Val Accuracy: 0.9514
Epoch 919/1200, Train Loss: 0.0628, Train Accuracy: 0.9811, Val Loss: 0.1253, Val Accuracy: 0.9514
Epoch 920/1200, Train Loss: 0.0608, Train Accuracy: 0.9819, Val Loss: 0.1253, Val Accuracy: 0.9514
Epoch 921/1200, Train Loss: 0.0614, Train Accuracy: 0.9811, Val Loss: 0.1252, Val Accuracy: 0.9516
Epoch 922/1200, Train Loss: 0.0608, Train Accuracy: 0.9813, Val Loss: 0.1251, Val Accuracy: 0.9514
Epoch 923/1200, Train Loss: 0.0630, Train Accuracy: 0.9813, Val Loss: 0.1252, Val Accuracy: 0.9516
Epoch 924/1200, Train Loss: 0.0612, Train Accuracy: 0.9816, Val Loss: 0.1252, Val Accuracy: 0.9514
Epoch 925/1200, Train Loss: 0.0622, Train Accuracy: 0.9810, Val Loss: 0.1251, Val Accuracy: 0.9514
Epoch 926/1200, Train Loss: 0.0606, Train Accuracy: 0.9816, Val Loss: 0.1251, Val Accuracy: 0.9514
Epoch 927/1200, Train Loss: 0.0595, Train Accuracy: 0.9820, Val Loss: 0.1250, Val Accuracy: 0.9514
Epoch 928/1200, Train Loss: 0.0592, Train Accuracy: 0.9827, Val Loss: 0.1252, Val Accuracy: 0.9514
Epoch 929/1200, Train Loss: 0.0599, Train Accuracy: 0.9826, Val Loss: 0.1251, Val Accuracy: 0.9516
Epoch 930/1200, Train Loss: 0.0596, Train Accuracy: 0.9811, Val Loss: 0.1251, Val Accuracy: 0.9514
Epoch 931/1200, Train Loss: 0.0605, Train Accuracy: 0.9808, Val Loss: 0.1251, Val Accuracy: 0.9514
Epoch 932/1200, Train Loss: 0.0603, Train Accuracy: 0.9816, Val Loss: 0.1250, Val Accuracy: 0.9512
Epoch 933/1200, Train Loss: 0.0603, Train Accuracy: 0.9810, Val Loss: 0.1249, Val Accuracy: 0.9514
Epoch 934/1200, Train Loss: 0.0588, Train Accuracy: 0.9828, Val Loss: 0.1250, Val Accuracy: 0.9514
Epoch 935/1200, Train Loss: 0.0590, Train Accuracy: 0.9820, Val Loss: 0.1249, Val Accuracy: 0.9512
Epoch 936/1200, Train Loss: 0.0596, Train Accuracy: 0.9821, Val Loss: 0.1249, Val Accuracy: 0.9514
Epoch 937/1200, Train Loss: 0.0575, Train Accuracy: 0.9830, Val Loss: 0.1250, Val Accuracy: 0.9514
Epoch 938/1200, Train Loss: 0.0586, Train Accuracy: 0.9825, Val Loss: 0.1249, Val Accuracy: 0.9514
Epoch 939/1200, Train Loss: 0.0590, Train Accuracy: 0.9828, Val Loss: 0.1249, Val Accuracy: 0.9516
Epoch 940/1200, Train Loss: 0.0582, Train Accuracy: 0.9829, Val Loss: 0.1250, Val Accuracy: 0.9516
Epoch 941/1200, Train Loss: 0.0586, Train Accuracy: 0.9827, Val Loss: 0.1250, Val Accuracy: 0.9514
Epoch 942/1200, Train Loss: 0.0577, Train Accuracy: 0.9830, Val Loss: 0.1250, Val Accuracy: 0.9514
Epoch 943/1200, Train Loss: 0.0580, Train Accuracy: 0.9816, Val Loss: 0.1251, Val Accuracy: 0.9514
Epoch 944/1200, Train Loss: 0.0566, Train Accuracy: 0.9829, Val Loss: 0.1249, Val Accuracy: 0.9518
Epoch 945/1200, Train Loss: 0.0586, Train Accuracy: 0.9822, Val Loss: 0.1253, Val Accuracy: 0.9518
Epoch 946/1200, Train Loss: 0.0587, Train Accuracy: 0.9827, Val Loss: 0.1252, Val Accuracy: 0.9514
Epoch 947/1200, Train Loss: 0.0570, Train Accuracy: 0.9829, Val Loss: 0.1252, Val Accuracy: 0.9516
Epoch 948/1200, Train Loss: 0.0559, Train Accuracy: 0.9841, Val Loss: 0.1252, Val Accuracy: 0.9516
Epoch 949/1200, Train Loss: 0.0576, Train Accuracy: 0.9818, Val Loss: 0.1252, Val Accuracy: 0.9516
Epoch 950/1200, Train Loss: 0.0570, Train Accuracy: 0.9830, Val Loss: 0.1254, Val Accuracy: 0.9520
Epoch 951/1200, Train Loss: 0.0552, Train Accuracy: 0.9833, Val Loss: 0.1253, Val Accuracy: 0.9518
Epoch 952/1200, Train Loss: 0.0559, Train Accuracy: 0.9833, Val Loss: 0.1253, Val Accuracy: 0.9516
Epoch 953/1200, Train Loss: 0.0566, Train Accuracy: 0.9834, Val Loss: 0.1254, Val Accuracy: 0.9516
Epoch 954/1200, Train Loss: 0.0559, Train Accuracy: 0.9835, Val Loss: 0.1254, Val Accuracy: 0.9520
Epoch 955/1200, Train Loss: 0.0564, Train Accuracy: 0.9826, Val Loss: 0.1253, Val Accuracy: 0.9518
Epoch 956/1200, Train Loss: 0.0554, Train Accuracy: 0.9840, Val Loss: 0.1253, Val Accuracy: 0.9522
Epoch 957/1200, Train Loss: 0.0572, Train Accuracy: 0.9828, Val Loss: 0.1253, Val Accuracy: 0.9520
Epoch 958/1200, Train Loss: 0.0571, Train Accuracy: 0.9831, Val Loss: 0.1252, Val Accuracy: 0.9518
Epoch 959/1200, Train Loss: 0.0567, Train Accuracy: 0.9837, Val Loss: 0.1252, Val Accuracy: 0.9516
Epoch 960/1200, Train Loss: 0.0566, Train Accuracy: 0.9831, Val Loss: 0.1251, Val Accuracy: 0.9516
Epoch 961/1200, Train Loss: 0.0545, Train Accuracy: 0.9840, Val Loss: 0.1251, Val Accuracy: 0.9516
Epoch 962/1200, Train Loss: 0.0565, Train Accuracy: 0.9832, Val Loss: 0.1252, Val Accuracy: 0.9520
Epoch 963/1200, Train Loss: 0.0563, Train Accuracy: 0.9831, Val Loss: 0.1251, Val Accuracy: 0.9516
Epoch 964/1200, Train Loss: 0.0569, Train Accuracy: 0.9827, Val Loss: 0.1251, Val Accuracy: 0.9516
Epoch 965/1200, Train Loss: 0.0553, Train Accuracy: 0.9836, Val Loss: 0.1251, Val Accuracy: 0.9516
Epoch 966/1200, Train Loss: 0.0551, Train Accuracy: 0.9840, Val Loss: 0.1252, Val Accuracy: 0.9520
Epoch 967/1200, Train Loss: 0.0532, Train Accuracy: 0.9852, Val Loss: 0.1252, Val Accuracy: 0.9518
Epoch 968/1200, Train Loss: 0.0546, Train Accuracy: 0.9842, Val Loss: 0.1251, Val Accuracy: 0.9518
Epoch 969/1200, Train Loss: 0.0537, Train Accuracy: 0.9840, Val Loss: 0.1251, Val Accuracy: 0.9518
Epoch 970/1200, Train Loss: 0.0543, Train Accuracy: 0.9839, Val Loss: 0.1253, Val Accuracy: 0.9518
Epoch 971/1200, Train Loss: 0.0549, Train Accuracy: 0.9839, Val Loss: 0.1253, Val Accuracy: 0.9520
Epoch 972/1200, Train Loss: 0.0527, Train Accuracy: 0.9852, Val Loss: 0.1253, Val Accuracy: 0.9520
Epoch 973/1200, Train Loss: 0.0539, Train Accuracy: 0.9852, Val Loss: 0.1252, Val Accuracy: 0.9518
Epoch 974/1200, Train Loss: 0.0531, Train Accuracy: 0.9849, Val Loss: 0.1252, Val Accuracy: 0.9518
Epoch 975/1200, Train Loss: 0.0536, Train Accuracy: 0.9840, Val Loss: 0.1253, Val Accuracy: 0.9518
Epoch 976/1200, Train Loss: 0.0527, Train Accuracy: 0.9848, Val Loss: 0.1253, Val Accuracy: 0.9518
Epoch 977/1200, Train Loss: 0.0531, Train Accuracy: 0.9843, Val Loss: 0.1254, Val Accuracy: 0.9522
Epoch 978/1200, Train Loss: 0.0534, Train Accuracy: 0.9840, Val Loss: 0.1252, Val Accuracy: 0.9520
Epoch 979/1200, Train Loss: 0.0534, Train Accuracy: 0.9843, Val Loss: 0.1253, Val Accuracy: 0.9520
Epoch 980/1200, Train Loss: 0.0531, Train Accuracy: 0.9845, Val Loss: 0.1253, Val Accuracy: 0.9522
Epoch 981/1200, Train Loss: 0.0530, Train Accuracy: 0.9838, Val Loss: 0.1252, Val Accuracy: 0.9520
Epoch 982/1200, Train Loss: 0.0534, Train Accuracy: 0.9847, Val Loss: 0.1252, Val Accuracy: 0.9524
Epoch 983/1200, Train Loss: 0.0551, Train Accuracy: 0.9838, Val Loss: 0.1253, Val Accuracy: 0.9524
Epoch 984/1200, Train Loss: 0.0527, Train Accuracy: 0.9845, Val Loss: 0.1254, Val Accuracy: 0.9522
Epoch 985/1200, Train Loss: 0.0518, Train Accuracy: 0.9848, Val Loss: 0.1255, Val Accuracy: 0.9524
Epoch 986/1200, Train Loss: 0.0526, Train Accuracy: 0.9847, Val Loss: 0.1253, Val Accuracy: 0.9520
Epoch 987/1200, Train Loss: 0.0524, Train Accuracy: 0.9847, Val Loss: 0.1253, Val Accuracy: 0.9520
Epoch 988/1200, Train Loss: 0.0528, Train Accuracy: 0.9848, Val Loss: 0.1254, Val Accuracy: 0.9520
Epoch 989/1200, Train Loss: 0.0531, Train Accuracy: 0.9840, Val Loss: 0.1254, Val Accuracy: 0.9524
Epoch 990/1200, Train Loss: 0.0510, Train Accuracy: 0.9855, Val Loss: 0.1254, Val Accuracy: 0.9524
Epoch 991/1200, Train Loss: 0.0521, Train Accuracy: 0.9849, Val Loss: 0.1256, Val Accuracy: 0.9526
Epoch 992/1200, Train Loss: 0.0507, Train Accuracy: 0.9856, Val Loss: 0.1253, Val Accuracy: 0.9520
Epoch 993/1200, Train Loss: 0.0517, Train Accuracy: 0.9853, Val Loss: 0.1253, Val Accuracy: 0.9520
Epoch 994/1200, Train Loss: 0.0514, Train Accuracy: 0.9847, Val Loss: 0.1253, Val Accuracy: 0.9520
Epoch 995/1200, Train Loss: 0.0526, Train Accuracy: 0.9845, Val Loss: 0.1255, Val Accuracy: 0.9524
Epoch 996/1200, Train Loss: 0.0512, Train Accuracy: 0.9854, Val Loss: 0.1254, Val Accuracy: 0.9526
Epoch 997/1200, Train Loss: 0.0524, Train Accuracy: 0.9849, Val Loss: 0.1254, Val Accuracy: 0.9528
Epoch 998/1200, Train Loss: 0.0529, Train Accuracy: 0.9851, Val Loss: 0.1252, Val Accuracy: 0.9520
Epoch 999/1200, Train Loss: 0.0502, Train Accuracy: 0.9865, Val Loss: 0.1255, Val Accuracy: 0.9528
Epoch 1000/1200, Train Loss: 0.0523, Train Accuracy: 0.9848, Val Loss: 0.1254, Val Accuracy: 0.9528
Epoch 1001/1200, Train Loss: 0.0506, Train Accuracy: 0.9848, Val Loss: 0.1255, Val Accuracy: 0.9526
Epoch 1002/1200, Train Loss: 0.0504, Train Accuracy: 0.9861, Val Loss: 0.1254, Val Accuracy: 0.9526
Epoch 1003/1200, Train Loss: 0.0497, Train Accuracy: 0.9861, Val Loss: 0.1254, Val Accuracy: 0.9524
Epoch 1004/1200, Train Loss: 0.0501, Train Accuracy: 0.9848, Val Loss: 0.1255, Val Accuracy: 0.9526
Epoch 1005/1200, Train Loss: 0.0505, Train Accuracy: 0.9859, Val Loss: 0.1254, Val Accuracy: 0.9526
Epoch 1006/1200, Train Loss: 0.0495, Train Accuracy: 0.9862, Val Loss: 0.1254, Val Accuracy: 0.9526
Epoch 1007/1200, Train Loss: 0.0502, Train Accuracy: 0.9851, Val Loss: 0.1256, Val Accuracy: 0.9526
Epoch 1008/1200, Train Loss: 0.0498, Train Accuracy: 0.9860, Val Loss: 0.1256, Val Accuracy: 0.9528
Epoch 1009/1200, Train Loss: 0.0489, Train Accuracy: 0.9861, Val Loss: 0.1258, Val Accuracy: 0.9522
Epoch 1010/1200, Train Loss: 0.0484, Train Accuracy: 0.9864, Val Loss: 0.1256, Val Accuracy: 0.9528
Epoch 1011/1200, Train Loss: 0.0501, Train Accuracy: 0.9855, Val Loss: 0.1257, Val Accuracy: 0.9524
Epoch 1012/1200, Train Loss: 0.0489, Train Accuracy: 0.9860, Val Loss: 0.1257, Val Accuracy: 0.9524
Epoch 1013/1200, Train Loss: 0.0483, Train Accuracy: 0.9866, Val Loss: 0.1256, Val Accuracy: 0.9526
Epoch 1014/1200, Train Loss: 0.0496, Train Accuracy: 0.9854, Val Loss: 0.1255, Val Accuracy: 0.9526
Epoch 1015/1200, Train Loss: 0.0486, Train Accuracy: 0.9867, Val Loss: 0.1257, Val Accuracy: 0.9526
Epoch 1016/1200, Train Loss: 0.0488, Train Accuracy: 0.9862, Val Loss: 0.1256, Val Accuracy: 0.9526
Epoch 1017/1200, Train Loss: 0.0487, Train Accuracy: 0.9866, Val Loss: 0.1255, Val Accuracy: 0.9530
Epoch 1018/1200, Train Loss: 0.0481, Train Accuracy: 0.9859, Val Loss: 0.1257, Val Accuracy: 0.9526
Epoch 1019/1200, Train Loss: 0.0499, Train Accuracy: 0.9862, Val Loss: 0.1256, Val Accuracy: 0.9526
Epoch 1020/1200, Train Loss: 0.0497, Train Accuracy: 0.9862, Val Loss: 0.1257, Val Accuracy: 0.9526
Epoch 1021/1200, Train Loss: 0.0478, Train Accuracy: 0.9865, Val Loss: 0.1258, Val Accuracy: 0.9530
Epoch 1022/1200, Train Loss: 0.0480, Train Accuracy: 0.9864, Val Loss: 0.1258, Val Accuracy: 0.9528
Epoch 1023/1200, Train Loss: 0.0489, Train Accuracy: 0.9867, Val Loss: 0.1258, Val Accuracy: 0.9530
Epoch 1024/1200, Train Loss: 0.0478, Train Accuracy: 0.9866, Val Loss: 0.1259, Val Accuracy: 0.9528
Epoch 1025/1200, Train Loss: 0.0487, Train Accuracy: 0.9860, Val Loss: 0.1259, Val Accuracy: 0.9528
Epoch 1026/1200, Train Loss: 0.0473, Train Accuracy: 0.9866, Val Loss: 0.1262, Val Accuracy: 0.9526
Epoch 1027/1200, Train Loss: 0.0470, Train Accuracy: 0.9869, Val Loss: 0.1260, Val Accuracy: 0.9528
Epoch 1028/1200, Train Loss: 0.0473, Train Accuracy: 0.9868, Val Loss: 0.1261, Val Accuracy: 0.9528
Epoch 1029/1200, Train Loss: 0.0480, Train Accuracy: 0.9854, Val Loss: 0.1261, Val Accuracy: 0.9524
Epoch 1030/1200, Train Loss: 0.0483, Train Accuracy: 0.9860, Val Loss: 0.1260, Val Accuracy: 0.9528
Epoch 1031/1200, Train Loss: 0.0480, Train Accuracy: 0.9868, Val Loss: 0.1259, Val Accuracy: 0.9530
Epoch 1032/1200, Train Loss: 0.0463, Train Accuracy: 0.9874, Val Loss: 0.1260, Val Accuracy: 0.9530
Epoch 1033/1200, Train Loss: 0.0468, Train Accuracy: 0.9869, Val Loss: 0.1262, Val Accuracy: 0.9526
Epoch 1034/1200, Train Loss: 0.0476, Train Accuracy: 0.9861, Val Loss: 0.1263, Val Accuracy: 0.9526
Epoch 1035/1200, Train Loss: 0.0471, Train Accuracy: 0.9870, Val Loss: 0.1260, Val Accuracy: 0.9530
Epoch 1036/1200, Train Loss: 0.0471, Train Accuracy: 0.9869, Val Loss: 0.1258, Val Accuracy: 0.9531
Epoch 1037/1200, Train Loss: 0.0452, Train Accuracy: 0.9870, Val Loss: 0.1259, Val Accuracy: 0.9530
Epoch 1038/1200, Train Loss: 0.0458, Train Accuracy: 0.9875, Val Loss: 0.1260, Val Accuracy: 0.9530
Epoch 1039/1200, Train Loss: 0.0476, Train Accuracy: 0.9864, Val Loss: 0.1260, Val Accuracy: 0.9530
Epoch 1040/1200, Train Loss: 0.0467, Train Accuracy: 0.9869, Val Loss: 0.1261, Val Accuracy: 0.9530
Epoch 1041/1200, Train Loss: 0.0464, Train Accuracy: 0.9880, Val Loss: 0.1260, Val Accuracy: 0.9530
Epoch 1042/1200, Train Loss: 0.0473, Train Accuracy: 0.9860, Val Loss: 0.1261, Val Accuracy: 0.9530
Epoch 1043/1200, Train Loss: 0.0458, Train Accuracy: 0.9876, Val Loss: 0.1263, Val Accuracy: 0.9528
Epoch 1044/1200, Train Loss: 0.0466, Train Accuracy: 0.9869, Val Loss: 0.1262, Val Accuracy: 0.9530
Epoch 1045/1200, Train Loss: 0.0459, Train Accuracy: 0.9875, Val Loss: 0.1263, Val Accuracy: 0.9530
Epoch 1046/1200, Train Loss: 0.0456, Train Accuracy: 0.9874, Val Loss: 0.1265, Val Accuracy: 0.9530
Epoch 1047/1200, Train Loss: 0.0451, Train Accuracy: 0.9884, Val Loss: 0.1264, Val Accuracy: 0.9531
Epoch 1048/1200, Train Loss: 0.0448, Train Accuracy: 0.9872, Val Loss: 0.1261, Val Accuracy: 0.9530
Epoch 1049/1200, Train Loss: 0.0450, Train Accuracy: 0.9874, Val Loss: 0.1263, Val Accuracy: 0.9530
Epoch 1050/1200, Train Loss: 0.0458, Train Accuracy: 0.9878, Val Loss: 0.1261, Val Accuracy: 0.9530
Epoch 1051/1200, Train Loss: 0.0453, Train Accuracy: 0.9876, Val Loss: 0.1260, Val Accuracy: 0.9530
Epoch 1052/1200, Train Loss: 0.0446, Train Accuracy: 0.9878, Val Loss: 0.1262, Val Accuracy: 0.9530
Epoch 1053/1200, Train Loss: 0.0448, Train Accuracy: 0.9871, Val Loss: 0.1263, Val Accuracy: 0.9530
Epoch 1054/1200, Train Loss: 0.0453, Train Accuracy: 0.9879, Val Loss: 0.1262, Val Accuracy: 0.9530
Epoch 1055/1200, Train Loss: 0.0449, Train Accuracy: 0.9879, Val Loss: 0.1262, Val Accuracy: 0.9530
Epoch 1056/1200, Train Loss: 0.0452, Train Accuracy: 0.9868, Val Loss: 0.1261, Val Accuracy: 0.9530
Epoch 1057/1200, Train Loss: 0.0439, Train Accuracy: 0.9877, Val Loss: 0.1263, Val Accuracy: 0.9530
Epoch 1058/1200, Train Loss: 0.0437, Train Accuracy: 0.9883, Val Loss: 0.1263, Val Accuracy: 0.9530
Epoch 1059/1200, Train Loss: 0.0430, Train Accuracy: 0.9886, Val Loss: 0.1262, Val Accuracy: 0.9528
Epoch 1060/1200, Train Loss: 0.0441, Train Accuracy: 0.9880, Val Loss: 0.1264, Val Accuracy: 0.9528
Epoch 1061/1200, Train Loss: 0.0442, Train Accuracy: 0.9885, Val Loss: 0.1262, Val Accuracy: 0.9528
Epoch 1062/1200, Train Loss: 0.0418, Train Accuracy: 0.9889, Val Loss: 0.1263, Val Accuracy: 0.9530
Epoch 1063/1200, Train Loss: 0.0438, Train Accuracy: 0.9877, Val Loss: 0.1263, Val Accuracy: 0.9530
Epoch 1064/1200, Train Loss: 0.0436, Train Accuracy: 0.9880, Val Loss: 0.1264, Val Accuracy: 0.9530
Epoch 1065/1200, Train Loss: 0.0433, Train Accuracy: 0.9882, Val Loss: 0.1263, Val Accuracy: 0.9528
Epoch 1066/1200, Train Loss: 0.0437, Train Accuracy: 0.9878, Val Loss: 0.1263, Val Accuracy: 0.9526
Epoch 1067/1200, Train Loss: 0.0432, Train Accuracy: 0.9879, Val Loss: 0.1262, Val Accuracy: 0.9530
Epoch 1068/1200, Train Loss: 0.0437, Train Accuracy: 0.9876, Val Loss: 0.1262, Val Accuracy: 0.9530
Epoch 1069/1200, Train Loss: 0.0422, Train Accuracy: 0.9890, Val Loss: 0.1262, Val Accuracy: 0.9531
Epoch 1070/1200, Train Loss: 0.0435, Train Accuracy: 0.9875, Val Loss: 0.1262, Val Accuracy: 0.9531
Epoch 1071/1200, Train Loss: 0.0420, Train Accuracy: 0.9891, Val Loss: 0.1263, Val Accuracy: 0.9531
Epoch 1072/1200, Train Loss: 0.0425, Train Accuracy: 0.9886, Val Loss: 0.1262, Val Accuracy: 0.9531
Epoch 1073/1200, Train Loss: 0.0425, Train Accuracy: 0.9889, Val Loss: 0.1263, Val Accuracy: 0.9531
Epoch 1074/1200, Train Loss: 0.0428, Train Accuracy: 0.9884, Val Loss: 0.1266, Val Accuracy: 0.9531
Epoch 1075/1200, Train Loss: 0.0422, Train Accuracy: 0.9888, Val Loss: 0.1264, Val Accuracy: 0.9531
Epoch 1076/1200, Train Loss: 0.0419, Train Accuracy: 0.9888, Val Loss: 0.1265, Val Accuracy: 0.9531
Epoch 1077/1200, Train Loss: 0.0429, Train Accuracy: 0.9883, Val Loss: 0.1267, Val Accuracy: 0.9533
Epoch 1078/1200, Train Loss: 0.0425, Train Accuracy: 0.9881, Val Loss: 0.1267, Val Accuracy: 0.9531
Epoch 1079/1200, Train Loss: 0.0413, Train Accuracy: 0.9893, Val Loss: 0.1268, Val Accuracy: 0.9531
Epoch 1080/1200, Train Loss: 0.0414, Train Accuracy: 0.9880, Val Loss: 0.1267, Val Accuracy: 0.9533
Epoch 1081/1200, Train Loss: 0.0424, Train Accuracy: 0.9879, Val Loss: 0.1267, Val Accuracy: 0.9533
Epoch 1082/1200, Train Loss: 0.0416, Train Accuracy: 0.9887, Val Loss: 0.1266, Val Accuracy: 0.9533
Epoch 1083/1200, Train Loss: 0.0416, Train Accuracy: 0.9882, Val Loss: 0.1266, Val Accuracy: 0.9531
Epoch 1084/1200, Train Loss: 0.0425, Train Accuracy: 0.9883, Val Loss: 0.1264, Val Accuracy: 0.9530
Epoch 1085/1200, Train Loss: 0.0411, Train Accuracy: 0.9889, Val Loss: 0.1264, Val Accuracy: 0.9528
Epoch 1086/1200, Train Loss: 0.0415, Train Accuracy: 0.9889, Val Loss: 0.1266, Val Accuracy: 0.9530
Epoch 1087/1200, Train Loss: 0.0411, Train Accuracy: 0.9893, Val Loss: 0.1267, Val Accuracy: 0.9530
Epoch 1088/1200, Train Loss: 0.0407, Train Accuracy: 0.9896, Val Loss: 0.1265, Val Accuracy: 0.9530
Epoch 1089/1200, Train Loss: 0.0404, Train Accuracy: 0.9895, Val Loss: 0.1267, Val Accuracy: 0.9530
Epoch 1090/1200, Train Loss: 0.0409, Train Accuracy: 0.9894, Val Loss: 0.1268, Val Accuracy: 0.9530
Epoch 1091/1200, Train Loss: 0.0403, Train Accuracy: 0.9890, Val Loss: 0.1270, Val Accuracy: 0.9531
Epoch 1092/1200, Train Loss: 0.0400, Train Accuracy: 0.9896, Val Loss: 0.1269, Val Accuracy: 0.9531
Epoch 1093/1200, Train Loss: 0.0422, Train Accuracy: 0.9878, Val Loss: 0.1271, Val Accuracy: 0.9531
Epoch 1094/1200, Train Loss: 0.0407, Train Accuracy: 0.9886, Val Loss: 0.1270, Val Accuracy: 0.9531
Epoch 1095/1200, Train Loss: 0.0411, Train Accuracy: 0.9895, Val Loss: 0.1271, Val Accuracy: 0.9531
Epoch 1096/1200, Train Loss: 0.0399, Train Accuracy: 0.9883, Val Loss: 0.1270, Val Accuracy: 0.9533
Epoch 1097/1200, Train Loss: 0.0402, Train Accuracy: 0.9894, Val Loss: 0.1270, Val Accuracy: 0.9533
Epoch 1098/1200, Train Loss: 0.0408, Train Accuracy: 0.9885, Val Loss: 0.1273, Val Accuracy: 0.9531
Epoch 1099/1200, Train Loss: 0.0404, Train Accuracy: 0.9895, Val Loss: 0.1271, Val Accuracy: 0.9533
Epoch 1100/1200, Train Loss: 0.0398, Train Accuracy: 0.9892, Val Loss: 0.1271, Val Accuracy: 0.9531
Epoch 1101/1200, Train Loss: 0.0384, Train Accuracy: 0.9913, Val Loss: 0.1270, Val Accuracy: 0.9530
Epoch 1102/1200, Train Loss: 0.0400, Train Accuracy: 0.9892, Val Loss: 0.1273, Val Accuracy: 0.9531
Epoch 1103/1200, Train Loss: 0.0408, Train Accuracy: 0.9888, Val Loss: 0.1273, Val Accuracy: 0.9531
Epoch 1104/1200, Train Loss: 0.0410, Train Accuracy: 0.9890, Val Loss: 0.1272, Val Accuracy: 0.9533
Epoch 1105/1200, Train Loss: 0.0403, Train Accuracy: 0.9897, Val Loss: 0.1270, Val Accuracy: 0.9530
Epoch 1106/1200, Train Loss: 0.0393, Train Accuracy: 0.9900, Val Loss: 0.1272, Val Accuracy: 0.9531
Epoch 1107/1200, Train Loss: 0.0390, Train Accuracy: 0.9899, Val Loss: 0.1271, Val Accuracy: 0.9530
Epoch 1108/1200, Train Loss: 0.0374, Train Accuracy: 0.9900, Val Loss: 0.1270, Val Accuracy: 0.9528
Epoch 1109/1200, Train Loss: 0.0377, Train Accuracy: 0.9906, Val Loss: 0.1272, Val Accuracy: 0.9530
Epoch 1110/1200, Train Loss: 0.0393, Train Accuracy: 0.9891, Val Loss: 0.1272, Val Accuracy: 0.9530
Epoch 1111/1200, Train Loss: 0.0391, Train Accuracy: 0.9900, Val Loss: 0.1274, Val Accuracy: 0.9530
Epoch 1112/1200, Train Loss: 0.0368, Train Accuracy: 0.9915, Val Loss: 0.1274, Val Accuracy: 0.9531
Epoch 1113/1200, Train Loss: 0.0382, Train Accuracy: 0.9904, Val Loss: 0.1273, Val Accuracy: 0.9530
Epoch 1114/1200, Train Loss: 0.0382, Train Accuracy: 0.9896, Val Loss: 0.1275, Val Accuracy: 0.9530
Epoch 1115/1200, Train Loss: 0.0387, Train Accuracy: 0.9899, Val Loss: 0.1274, Val Accuracy: 0.9530
Epoch 1116/1200, Train Loss: 0.0385, Train Accuracy: 0.9907, Val Loss: 0.1275, Val Accuracy: 0.9531
Epoch 1117/1200, Train Loss: 0.0386, Train Accuracy: 0.9905, Val Loss: 0.1277, Val Accuracy: 0.9531
Epoch 1118/1200, Train Loss: 0.0390, Train Accuracy: 0.9894, Val Loss: 0.1277, Val Accuracy: 0.9531
Epoch 1119/1200, Train Loss: 0.0376, Train Accuracy: 0.9904, Val Loss: 0.1278, Val Accuracy: 0.9531
Epoch 1120/1200, Train Loss: 0.0375, Train Accuracy: 0.9901, Val Loss: 0.1276, Val Accuracy: 0.9531
Epoch 1121/1200, Train Loss: 0.0379, Train Accuracy: 0.9895, Val Loss: 0.1276, Val Accuracy: 0.9531
Epoch 1122/1200, Train Loss: 0.0384, Train Accuracy: 0.9905, Val Loss: 0.1277, Val Accuracy: 0.9531
Epoch 1123/1200, Train Loss: 0.0382, Train Accuracy: 0.9895, Val Loss: 0.1276, Val Accuracy: 0.9530
Epoch 1124/1200, Train Loss: 0.0367, Train Accuracy: 0.9909, Val Loss: 0.1275, Val Accuracy: 0.9530
Epoch 1125/1200, Train Loss: 0.0382, Train Accuracy: 0.9902, Val Loss: 0.1276, Val Accuracy: 0.9531
Epoch 1126/1200, Train Loss: 0.0368, Train Accuracy: 0.9906, Val Loss: 0.1276, Val Accuracy: 0.9533
Epoch 1127/1200, Train Loss: 0.0383, Train Accuracy: 0.9900, Val Loss: 0.1278, Val Accuracy: 0.9531
Epoch 1128/1200, Train Loss: 0.0384, Train Accuracy: 0.9895, Val Loss: 0.1276, Val Accuracy: 0.9526
Epoch 1129/1200, Train Loss: 0.0369, Train Accuracy: 0.9902, Val Loss: 0.1280, Val Accuracy: 0.9531
Epoch 1130/1200, Train Loss: 0.0371, Train Accuracy: 0.9904, Val Loss: 0.1279, Val Accuracy: 0.9531
Epoch 1131/1200, Train Loss: 0.0366, Train Accuracy: 0.9900, Val Loss: 0.1279, Val Accuracy: 0.9531
Epoch 1132/1200, Train Loss: 0.0368, Train Accuracy: 0.9905, Val Loss: 0.1278, Val Accuracy: 0.9530
Epoch 1133/1200, Train Loss: 0.0374, Train Accuracy: 0.9897, Val Loss: 0.1277, Val Accuracy: 0.9524
Epoch 1134/1200, Train Loss: 0.0360, Train Accuracy: 0.9911, Val Loss: 0.1276, Val Accuracy: 0.9524
Epoch 1135/1200, Train Loss: 0.0359, Train Accuracy: 0.9905, Val Loss: 0.1277, Val Accuracy: 0.9524
Epoch 1136/1200, Train Loss: 0.0363, Train Accuracy: 0.9901, Val Loss: 0.1278, Val Accuracy: 0.9526
Epoch 1137/1200, Train Loss: 0.0369, Train Accuracy: 0.9898, Val Loss: 0.1282, Val Accuracy: 0.9530
Epoch 1138/1200, Train Loss: 0.0362, Train Accuracy: 0.9909, Val Loss: 0.1280, Val Accuracy: 0.9530
Epoch 1139/1200, Train Loss: 0.0358, Train Accuracy: 0.9912, Val Loss: 0.1281, Val Accuracy: 0.9530
Epoch 1140/1200, Train Loss: 0.0353, Train Accuracy: 0.9907, Val Loss: 0.1281, Val Accuracy: 0.9530
Epoch 1141/1200, Train Loss: 0.0367, Train Accuracy: 0.9903, Val Loss: 0.1281, Val Accuracy: 0.9530
Epoch 1142/1200, Train Loss: 0.0343, Train Accuracy: 0.9921, Val Loss: 0.1281, Val Accuracy: 0.9526
Epoch 1143/1200, Train Loss: 0.0342, Train Accuracy: 0.9913, Val Loss: 0.1280, Val Accuracy: 0.9526
Epoch 1144/1200, Train Loss: 0.0361, Train Accuracy: 0.9911, Val Loss: 0.1281, Val Accuracy: 0.9526
Epoch 1145/1200, Train Loss: 0.0359, Train Accuracy: 0.9914, Val Loss: 0.1282, Val Accuracy: 0.9526
Epoch 1146/1200, Train Loss: 0.0356, Train Accuracy: 0.9915, Val Loss: 0.1284, Val Accuracy: 0.9526
Epoch 1147/1200, Train Loss: 0.0345, Train Accuracy: 0.9921, Val Loss: 0.1281, Val Accuracy: 0.9526
Epoch 1148/1200, Train Loss: 0.0354, Train Accuracy: 0.9908, Val Loss: 0.1282, Val Accuracy: 0.9524
Epoch 1149/1200, Train Loss: 0.0354, Train Accuracy: 0.9909, Val Loss: 0.1282, Val Accuracy: 0.9526
Epoch 1150/1200, Train Loss: 0.0347, Train Accuracy: 0.9916, Val Loss: 0.1284, Val Accuracy: 0.9526
Epoch 1151/1200, Train Loss: 0.0348, Train Accuracy: 0.9912, Val Loss: 0.1284, Val Accuracy: 0.9526
Epoch 1152/1200, Train Loss: 0.0350, Train Accuracy: 0.9908, Val Loss: 0.1286, Val Accuracy: 0.9526
Epoch 1153/1200, Train Loss: 0.0343, Train Accuracy: 0.9914, Val Loss: 0.1285, Val Accuracy: 0.9524
Epoch 1154/1200, Train Loss: 0.0352, Train Accuracy: 0.9908, Val Loss: 0.1285, Val Accuracy: 0.9524
Epoch 1155/1200, Train Loss: 0.0339, Train Accuracy: 0.9915, Val Loss: 0.1288, Val Accuracy: 0.9528
Epoch 1156/1200, Train Loss: 0.0344, Train Accuracy: 0.9914, Val Loss: 0.1286, Val Accuracy: 0.9522
Epoch 1157/1200, Train Loss: 0.0346, Train Accuracy: 0.9918, Val Loss: 0.1287, Val Accuracy: 0.9524
Epoch 1158/1200, Train Loss: 0.0351, Train Accuracy: 0.9909, Val Loss: 0.1285, Val Accuracy: 0.9524
Epoch 1159/1200, Train Loss: 0.0345, Train Accuracy: 0.9912, Val Loss: 0.1287, Val Accuracy: 0.9522
Epoch 1160/1200, Train Loss: 0.0337, Train Accuracy: 0.9918, Val Loss: 0.1289, Val Accuracy: 0.9524
Epoch 1161/1200, Train Loss: 0.0337, Train Accuracy: 0.9916, Val Loss: 0.1288, Val Accuracy: 0.9520
Epoch 1162/1200, Train Loss: 0.0348, Train Accuracy: 0.9915, Val Loss: 0.1288, Val Accuracy: 0.9520
Epoch 1163/1200, Train Loss: 0.0348, Train Accuracy: 0.9911, Val Loss: 0.1291, Val Accuracy: 0.9528
Epoch 1164/1200, Train Loss: 0.0347, Train Accuracy: 0.9913, Val Loss: 0.1291, Val Accuracy: 0.9526
Epoch 1165/1200, Train Loss: 0.0333, Train Accuracy: 0.9915, Val Loss: 0.1290, Val Accuracy: 0.9522
Epoch 1166/1200, Train Loss: 0.0341, Train Accuracy: 0.9912, Val Loss: 0.1291, Val Accuracy: 0.9522
Epoch 1167/1200, Train Loss: 0.0336, Train Accuracy: 0.9916, Val Loss: 0.1291, Val Accuracy: 0.9520
Epoch 1168/1200, Train Loss: 0.0336, Train Accuracy: 0.9921, Val Loss: 0.1291, Val Accuracy: 0.9522
Epoch 1169/1200, Train Loss: 0.0332, Train Accuracy: 0.9920, Val Loss: 0.1289, Val Accuracy: 0.9522
Epoch 1170/1200, Train Loss: 0.0342, Train Accuracy: 0.9909, Val Loss: 0.1288, Val Accuracy: 0.9524
Epoch 1171/1200, Train Loss: 0.0332, Train Accuracy: 0.9919, Val Loss: 0.1290, Val Accuracy: 0.9520
Epoch 1172/1200, Train Loss: 0.0322, Train Accuracy: 0.9925, Val Loss: 0.1291, Val Accuracy: 0.9520
Epoch 1173/1200, Train Loss: 0.0339, Train Accuracy: 0.9914, Val Loss: 0.1292, Val Accuracy: 0.9522
Epoch 1174/1200, Train Loss: 0.0323, Train Accuracy: 0.9923, Val Loss: 0.1291, Val Accuracy: 0.9520
Epoch 1175/1200, Train Loss: 0.0327, Train Accuracy: 0.9917, Val Loss: 0.1292, Val Accuracy: 0.9520
Epoch 1176/1200, Train Loss: 0.0322, Train Accuracy: 0.9925, Val Loss: 0.1294, Val Accuracy: 0.9520
Epoch 1177/1200, Train Loss: 0.0323, Train Accuracy: 0.9925, Val Loss: 0.1295, Val Accuracy: 0.9520
Epoch 1178/1200, Train Loss: 0.0343, Train Accuracy: 0.9914, Val Loss: 0.1293, Val Accuracy: 0.9520
Epoch 1179/1200, Train Loss: 0.0330, Train Accuracy: 0.9918, Val Loss: 0.1294, Val Accuracy: 0.9520
Epoch 1180/1200, Train Loss: 0.0324, Train Accuracy: 0.9914, Val Loss: 0.1295, Val Accuracy: 0.9520
Epoch 1181/1200, Train Loss: 0.0325, Train Accuracy: 0.9914, Val Loss: 0.1294, Val Accuracy: 0.9520
Epoch 1182/1200, Train Loss: 0.0334, Train Accuracy: 0.9911, Val Loss: 0.1295, Val Accuracy: 0.9520
Epoch 1183/1200, Train Loss: 0.0331, Train Accuracy: 0.9918, Val Loss: 0.1295, Val Accuracy: 0.9520
Epoch 1184/1200, Train Loss: 0.0325, Train Accuracy: 0.9921, Val Loss: 0.1298, Val Accuracy: 0.9522
Epoch 1185/1200, Train Loss: 0.0319, Train Accuracy: 0.9917, Val Loss: 0.1297, Val Accuracy: 0.9520
Epoch 1186/1200, Train Loss: 0.0320, Train Accuracy: 0.9923, Val Loss: 0.1297, Val Accuracy: 0.9520
Epoch 1187/1200, Train Loss: 0.0317, Train Accuracy: 0.9929, Val Loss: 0.1298, Val Accuracy: 0.9520
Epoch 1188/1200, Train Loss: 0.0319, Train Accuracy: 0.9923, Val Loss: 0.1298, Val Accuracy: 0.9520
Epoch 1189/1200, Train Loss: 0.0314, Train Accuracy: 0.9927, Val Loss: 0.1296, Val Accuracy: 0.9520
Epoch 1190/1200, Train Loss: 0.0324, Train Accuracy: 0.9918, Val Loss: 0.1295, Val Accuracy: 0.9520
Epoch 1191/1200, Train Loss: 0.0303, Train Accuracy: 0.9932, Val Loss: 0.1296, Val Accuracy: 0.9520
Epoch 1192/1200, Train Loss: 0.0317, Train Accuracy: 0.9922, Val Loss: 0.1297, Val Accuracy: 0.9522
Epoch 1193/1200, Train Loss: 0.0314, Train Accuracy: 0.9930, Val Loss: 0.1297, Val Accuracy: 0.9522
Epoch 1194/1200, Train Loss: 0.0314, Train Accuracy: 0.9922, Val Loss: 0.1296, Val Accuracy: 0.9520
Epoch 1195/1200, Train Loss: 0.0308, Train Accuracy: 0.9934, Val Loss: 0.1297, Val Accuracy: 0.9520
Epoch 1196/1200, Train Loss: 0.0324, Train Accuracy: 0.9913, Val Loss: 0.1299, Val Accuracy: 0.9522
Epoch 1197/1200, Train Loss: 0.0311, Train Accuracy: 0.9924, Val Loss: 0.1299, Val Accuracy: 0.9520
Epoch 1198/1200, Train Loss: 0.0304, Train Accuracy: 0.9932, Val Loss: 0.1299, Val Accuracy: 0.9522
Epoch 1199/1200, Train Loss: 0.0308, Train Accuracy: 0.9925, Val Loss: 0.1304, Val Accuracy: 0.9518
Epoch 1200/1200, Train Loss: 0.0304, Train Accuracy: 0.9926, Val Loss: 0.1302, Val Accuracy: 0.9520
In [ ]:
torch.save(model3.state_dict(), best_model_path3)
In [ ]:
from sklearn.model_selection import GridSearchCV
from sklearn.neural_network import MLPClassifier

# Create the neural network classifier
clf = MLPClassifier(random_state=42)

# Assuming you have performed a grid search and obtained the best estimator
# Define the parameter grid
param_grid = {
    'hidden_layer_sizes': [(30,30), (10,10,10)],
    'solver': ['adam', 'sgd'],
    'alpha': [0.001, 0.01],
}

# Create the GridSearchCV object
grid_search = GridSearchCV(clf, param_grid, scoring='accuracy', cv=2)

# Fit the GridSearchCV object to the data
grid_search.fit(X_train_all_nn, y_train_all_nn)


# Save the best estimator in best_nn
best_nn_all = grid_search.best_estimator_
print(best_nn_all)

c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(

MLPClassifier(alpha=0.001, hidden_layer_sizes=(30, 30), random_state=42,
              solver='sgd')

c:\Users\tommy\anaconda3\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  warnings.warn(
¶

Test for the model all datasets merged

In [ ]:
# Load the best model
model3.load_state_dict(torch.load('best_model_final3.pth'))

# Evaluate on test data
model3.eval()
X_test_all_tensor2 = torch.tensor(X_test_all.values, dtype=torch.float32)
y_test_all_tensor2 = torch.tensor(y_test_all.values, dtype=torch.float32).unsqueeze(1)


with torch.no_grad():
    test_output = model3(X_test_all_tensor2)
    test_loss = criterion(test_output, y_test_all_tensor2)
    test_predictions = torch.round(torch.sigmoid(test_output))
    test_correct = (test_predictions == y_test_all_tensor2).sum().item()
    test_total = y_test_all_tensor2.size(0)
    test_acc = test_correct / test_total

print(f'Test Loss: {test_loss.item():.4f}, Test Accuracy: {test_acc:.4f}')

Test Loss: 0.1287, Test Accuracy: 0.9565

¶

4.6.5) KNN

In [ ]:
best_knn_all = train_and_evaluate_knn(X_train_all, X_test_all, y_train_all, y_test_all)

Best hyperparameters:  {'n_neighbors': 2}
Accuracy: 0.6018871348212665
F1: 0.5387849484969519
Classification Report:
              precision    recall  f1-score   support

           0       0.58      0.74      0.65      5537
           1       0.64      0.47      0.54      5485

    accuracy                           0.60     11022
   macro avg       0.61      0.60      0.59     11022
weighted avg       0.61      0.60      0.59     11022

¶

4.6.6) Logistic Regression

In [ ]:
best_logreg_all = train_and_evaluate_logreg(X_train_all, X_test_all, y_train_all, y_test_all)

Best hyperparameters:  {'C': 0.5, 'penalty': 'l2'}
Accuracy: 0.9505534385773907
F1: 0.949866617606476
Classification Report:
              precision    recall  f1-score   support

           0       0.94      0.96      0.95      5537
           1       0.96      0.94      0.95      5485

    accuracy                           0.95     11022
   macro avg       0.95      0.95      0.95     11022
weighted avg       0.95      0.95      0.95     11022
In [ ]:
best_catboost_all=train_and_evaluate_catboost(X_train_all, X_test_all, y_train_all, y_test_all)

Best hyperparameters:  {'logging_level': 'Silent'}
Accuracy: 0.9682453275267646
F1: 0.9678957989359751
Classification Report:
              precision    recall  f1-score   support

           0       0.96      0.97      0.97      5537
           1       0.97      0.96      0.97      5485

    accuracy                           0.97     11022
   macro avg       0.97      0.97      0.97     11022
weighted avg       0.97      0.97      0.97     11022

¶

5 ) Voting Classifiers

Finally, we decided to join the 4 different models into a general one using as ensamble learning model.

To be consistent with the split of training and test data on which we should then have evaluated the performance of the model, we decided to join separately all the train and test split of the four datasets in order to build two big and completely independent training and test datasets.

In [ ]:
#now i want to build a voting classifier with the 4 best models

#before doing that i need to build the train and test dataset joining the ones i used for the models
#i will use the minmaxscaler to rescale the data

scaler = MinMaxScaler()
X_train_df_SS_MCF7.iloc[ : , :] = scaler.fit_transform(X_train_df_SS_MCF7)
X_test_df_SS_MCF7.iloc[ : , :] = scaler.fit_transform(X_test_df_SS_MCF7)

X_train_df_SS_HCC1806.iloc[ : , :] = scaler.fit_transform(X_train_df_SS_HCC1806)
X_test_df_SS_HCC1806.iloc[ : , :] = scaler.fit_transform(X_test_df_SS_HCC1806)

X_train_df_DS_MCF7.iloc[ : , :] = scaler.fit_transform(X_train_df_DS_MCF7)
X_test_df_DS_MCF7.iloc[ : , :] = scaler.fit_transform(X_test_df_DS_MCF7)

X_train_df_DS_HCC1806.iloc[ : , :] = scaler.fit_transform(X_train_df_DS_HCC1806)
X_test_df_DS_HCC1806.iloc[ : , :] = scaler.fit_transform(X_test_df_DS_HCC1806)

X_test = pd.concat([X_test_df_SS_MCF7, X_test_df_SS_HCC1806, X_test_df_DS_MCF7, X_test_df_DS_HCC1806])
X_train = pd.concat([X_train_df_SS_MCF7, X_train_df_SS_HCC1806, X_train_df_DS_MCF7, X_train_df_DS_HCC1806])

#i substitute the NaN values with 0
X_test.fillna(0, inplace=True)
X_train.fillna(0, inplace=True)

y_train = pd.concat([y_train_df_SS_MCF7, y_train_df_SS_HCC1806, y_train_df_DS_MCF7, y_train_df_DS_HCC1806])
y_test = pd.concat([y_test_df_SS_MCF7, y_test_df_SS_HCC1806, y_test_df_DS_MCF7, y_test_df_DS_HCC1806])

In order to join the four predictors, we decide to use a Voting classifier.

It is a powerful ensemble learning technique that combines the predictions of multiple classifiers to make more accurate and robust predictions.

We decide to use the 'soft' voting approach: in soft voting, the final prediction is the class with the highest average probability predicted by the base classifiers. For classifiers that output class probabilities, the probabilities are averaged across all base classifiers, and the class with the highest average probability is chosen as the final prediction.

¶

5.1) SVM

In [ ]:
from sklearn.ensemble import VotingClassifier

# Create the voting classifier
voting_clf_SVM = VotingClassifier(
    estimators=[
        ('svm ds hcc', best_svm_DS_HCC1806),
        ('svm ds mcf7', best_svm_DS_MCF7),
        ('svm ss hcc', best_svm_SS_HCC1806),
        ('svm ss mcf7', best_svm_SS_MCF7),
    ],
    voting='hard'  # Use majority voting
)

# Fit the voting classifier on the training data
voting_clf_SVM.fit(X_train, y_train)

# Evaluate the voting classifier on the test data
test_accuracy = voting_clf_SVM.score(X_test, y_test)
print("Test accuracy: ", test_accuracy)

Test accuracy:  0.9471842907121092

¶

5.2) XGB

In [ ]:
#ensamble the models

# Define the voting classifier
voting_clf_XGB = VotingClassifier(estimators=[('best_rf_SS_MCF7', best_rf_SS_MCF7), ('best_xgb_SS_HCC1806', best_xgb_SS_HCC1806),
                                           ('best_xgb_DS_HCC1806', best_xgb_DS_HCC1806), ('best_xgb_DS_MCF7', best_xgb_DS_MCF7)], voting='soft')

outer_cv = KFold(n_splits=5, shuffle=True, random_state=42)
# Perform the cross validation
scores = cross_val_score(voting_clf_XGB, X_train, y_train, cv=outer_cv)

# Print the mean accuracy of the nested cross validation
print("Mean accuracy of nested cross validation: ", np.mean(scores))

# Fit the voting classifier on the train dataset
voting_clf_XGB.fit(X_train, y_train)

#print the accuracy of the model
print("Accuracy of the model: ", voting_clf_XGB.score(X_test, y_test))

Mean accuracy of nested cross validation:  0.9599875973291393
Accuracy of the model:  0.9545495781547673

As a final consideration we can say that the general xgboost model had a better performance in the test set with respect to the Voting classifer made up of the 4 singluar preditors, but since the difference is minimal (only 0.5%), for the prediction on a completely new dataset we would recomment to still use both to also have a comparison of the effectiveness when they are supposed to predict the target of completely unseen data in which features could have different properties from the ones found in our datasets.

¶

5.3) KNN

In [ ]:
# Create the voting classifier
voting_clf_knn = VotingClassifier(
    estimators=[
        ('knn ds hcc', best_knn_DS_HCC1806),
        ('knn ds mcf7', best_knn_DS_MCF7),
        ('knn ss hcc', best_knn_SS_HCC1806),
        ('knn ss mcf7', best_knn_DS_MCF7),
    ],
    voting='hard'  # Use majority voting
)

# Fit the voting classifier on the training data
voting_clf_knn.fit(X_train, y_train)

# Evaluate the voting classifier on the test data
test_accuracy = voting_clf_knn.score(X_test, y_test)
print("Test accuracy: ", test_accuracy)

Test accuracy:  0.5579505699131023

¶

5.4) Logistic Regression

In [ ]:
# Create the voting classifier
voting_clf_logreg = VotingClassifier(
    estimators=[
        ('logreg ds hcc', best_logreg_DS_HCC1806),
        ('logreg ds mcf7', best_logreg_DS_MCF7),
        ('logreg ss hcc', best_logreg_SS_HCC1806),
        ('logreg ss mcf7', best_logreg_DS_MCF7),
    ],
    voting='hard'  # Use majority voting
)

# Fit the voting classifier on the training data
voting_clf_logreg.fit(X_train, y_train)

# Evaluate the voting classifier on the test data
test_accuracy = voting_clf_logreg.score(X_test, y_test)
print("Test accuracy: ", test_accuracy)

Test accuracy:  0.8599480871233495
In [ ]:
# Create the voting classifier
voting_clf_logreg2 = VotingClassifier(
    estimators=[
        ('logreg2 ss mcf', model_logreg_mc_SMART_ver2_SS_MCF),
        ('logreg2 ss hcc', model_logreg_hcc_SMART_ver2_SS_HCC),
        ('logreg2 ds mcf', model_logreg_mc_DROP_ver2_DS_MCF),
        ('logreg2 ds hcc', model_logreg_hcc_DROP_ver2_DS_HCC),
    ],
    voting='hard'  # Use majority voting
)

# Fit the voting classifier on the training data
voting_clf_logreg2.fit(X_train, y_train)

# Evaluate the voting classifier on the test data
test_accuracy = voting_clf_logreg2.score(X_test, y_test)
print("Test accuracy: ", test_accuracy)

Test accuracy:  0.8599480871233495

¶

5.5) catboost

In [ ]:
# Create the voting classifier
voting_clf_catboost = VotingClassifier(
    estimators=[
        ('catboost ds hcc', best_catboost_DS_HCC1806),
        ('catboost ds mcf7', best_catboost_DS_MCF7),
        ('catboost ss hcc', best_catboost_SS_HCC1806),
        ('catboost ss mcf7', best_catboost_DS_MCF7),
    ],
    voting='hard'  # Use majority voting
)

# Fit the voting classifier on the training data
voting_clf_catboost.fit(X_train, y_train)

# Evaluate the voting classifier on the test data
test_accuracy = voting_clf_catboost.score(X_test, y_test)
print("Test accuracy: ", test_accuracy)

Test accuracy:  0.960049655795057
In [ ]:
# Create the voting classifier
voting_clf_catboost2 = VotingClassifier(
    estimators=[
        ('catboost ss mcf', model_catboost_mc_SMART_ver2_SS_MCF),
        ('catboost ss hcc', model_catboost_hcc_SMART_ver2_SS_HCC),
        ('catboost ds mcf', model_catboost_mc_DROP_ver2_DS_MCF),
        ('catboost ds hcc', model_catboost_hcc_DROP_ver2_DS_HCC),
    ],
    voting='hard'  # Use majority voting
)

# Fit the voting classifier on the training data
voting_clf_catboost2.fit(X_train, y_train)

# Evaluate the voting classifier on the test data
test_accuracy = voting_clf_catboost2.score(X_test, y_test)
print("Test accuracy: ", test_accuracy)

Test accuracy:  0.9535041191739081

¶

6 ) Predictions

Now we want to make prediction. For each dataset we use the 3 best models made before.

So, for each dataset we watch to predictors with highest test accuracy and to put 0 or 1 for prediction "x" we take the mode value for it (ex: if there are two 0 and one 1, we have 0). For this purpose we use an odd number of predictors.

¶

6.1) Prediction on SmartSeq MCF7

¶

6.1.0) Loading dataset Start by opening the dataset "MCF7_SmartS_Filtered_Normalised_3000_Data_test_anonim"

In [ ]:
df_SS_MCF7_anon = pd.read_csv("SmartSeq/MCF7_SmartS_Filtered_Normalised_3000_Data_test_anonim.txt", delimiter="\ ",engine='python',index_col=0).T
print("Dataframe dimensions:", np.shape(df_SS_MCF7_anon))

Dataframe dimensions: (63, 3000)

¶

6.1.1) Random Forest

In [ ]:
predictions_SS_MCF7_rf = best_rf_SS_MCF7.predict(df_SS_MCF7_anon)
In [ ]:
# Convert predictions to a DataFrame and save as CSV
predictions_SS_MCF7_rf_df = pd.DataFrame(predictions_SS_MCF7_rf, columns=['Predictions'])
In [ ]:
predictions_SS_MCF7_rf_df
Out[ ]:
Predictions
0 1
1 1
2 0
3 0
4 0
... ...
58 1
59 1
60 1
61 0
62 0

63 rows × 1 columns

¶

6.1.2) Logistic Regression

In [ ]:
predictions_SS_MCF7_logreg = best_logreg_SS_MCF7.predict(df_SS_MCF7_anon)
In [ ]:
predictions_SS_MCF7_logreg
Out[ ]:
array([1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
       1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1,
       0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0])
In [ ]:
# Convert predictions to a DataFrame and save as CSV
predictions_SS_MCF7_logreg_df = pd.DataFrame(predictions_SS_MCF7_logreg, columns=['Predictions'])
In [ ]:
predictions_SS_MCF7_logreg_df
Out[ ]:
Predictions
0 1
1 1
2 0
3 0
4 0
... ...
58 1
59 1
60 1
61 0
62 0

63 rows × 1 columns

¶

6.1.3)SVM

In [ ]:
predictions_SS_MCF7_svm = best_svm_SS_MCF7.predict(df_SS_MCF7_anon)
In [ ]:
# Convert predictions to a DataFrame and save as CSV
predictions_SS_MCF7_svm = pd.DataFrame(predictions_SS_MCF7_svm, columns=['Predictions'])
In [ ]:
predictions_SS_MCF7_svm_array=np.array(predictions_SS_MCF7_svm)
predictions_SS_MCF7_svm_array
Out[ ]:
array([[1],
       [1],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [1],
       [0],
       [0],
       [0],
       [1],
       [1],
       [1],
       [1],
       [1],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [0],
       [0],
       [1],
       [1],
       [1],
       [1],
       [0],
       [0],
       [1],
       [1],
       [0],
       [0],
       [0],
       [0],
       [1],
       [1],
       [0],
       [1],
       [1],
       [1],
       [1],
       [0],
       [0],
       [1],
       [1],
       [1],
       [1],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [1],
       [1],
       [1],
       [1],
       [0],
       [0]])

¶

6.1.4) FINAL CONSIDERATION

In [ ]:
k=0
for i in range(63):
    if predictions_SS_MCF7_rf[i]!=predictions_SS_MCF7_logreg[i]:
        k=k+1
print("LOGISTIC VS RF", "\nnumber errors: ", k, "\npercentage correspondance: ", (63-k)/63)

LOGISTIC VS RF 
number errors:  0 
percentage correspondance:  1.0
In [ ]:
k=0
for i in range(63):
    if predictions_SS_MCF7_svm_array[i] != predictions_SS_MCF7_logreg[i]:
        k=k+1
print("LOGISTIC VS SVM", "\nnumber errors: ", k, "\npercentage correspondance: ", (63-k)/63)

LOGISTIC VS SVM 
number errors:  0 
percentage correspondance:  1.0
In [ ]:
k=0
for i in range(63):
    if predictions_SS_MCF7_rf[i]!=predictions_SS_MCF7_svm_array[i]:
        k=k+1
print("SVM VS RF", "\nnumber errors: ", k, "\npercentage correspondance: ", (63-k)/63)

SVM VS RF 
number errors:  0 
percentage correspondance:  1.0
In [ ]:
from scipy import stats

# Assuming predictions_SS_MCF7_rf, predictions_SS_MCF7_logreg, and predictions_SS_MCF7_svm_array are numpy arrays
predictions_SS_MCF7_rf = np.array(predictions_SS_MCF7_rf)
predictions_SS_MCF7_logreg = np.array(predictions_SS_MCF7_logreg)
predictions_SS_MCF7_svm_array = np.array(predictions_SS_MCF7_svm_array.T)

# Stack the predictions to create a 2D array (each row corresponds to the predictions for one instance)
stacked_predictions = np.vstack((predictions_SS_MCF7_rf, predictions_SS_MCF7_logreg, predictions_SS_MCF7_svm_array))

# Compute the mode along the columns
final_prediction_SS_MCF7, _ = stats.mode(stacked_predictions, axis=0)

# Since stats.mode returns a 2D array, we flatten it to get a 1D array
final_prediction_SS_MCF7 = final_prediction_SS_MCF7.flatten()

final_prediction_SS_MCF7
Out[ ]:
array([1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
       1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1,
       0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0],
      dtype=int64)
In [ ]:
final_prediction_SS_MCF7_df = pd.DataFrame(final_prediction_SS_MCF7, columns=['Predictions'])
final_prediction_SS_MCF7_df.to_csv('final_prediction_SS_MCF7.csv', index=True)
In [ ]:
k=0
for i in range(63):
    if final_prediction_SS_MCF7[i]!=predictions_SS_MCF7_svm_array[i]:
        k=k+1
print("Differences final_prediction_SS_MCF7 and SVM_SS_MCF7", k, "\npercentage correspondance final_prediction_SS_MCF7 and SVM_SS_MCF7: ", (63-k)/63)

SVM VS RF 
number errors:  0 
percentage correspondance:  1.0
In [ ]:
k=0
for i in range(63):
    if predictions_SS_MCF7_rf[i]!=final_prediction_SS_MCF7[i]:
        k=k+1
print("Differences final_prediction_SS_MCF7 and RF_SS_MCF7", k, "\npercentage correspondance final_prediction_SS_MCF7 and SRF_SS_MCF7: ", (63-k)/63)

SVM VS RF 
number errors:  0 
percentage correspondance:  1.0
In [ ]:
k=0
for i in range(63):
    if final_prediction_SS_MCF7[i]!=predictions_SS_MCF7_logreg[i]:
        k=k+1
print("Differences final_prediction_SS_MCF7 and Logistic_Regressino_SS_MCF7", k, "\npercentage correspondance final_prediction_SS_MCF7 and Logistic_Regressino_SS_MCF7: ", (63-k)/63)

SVM VS RF 
number errors:  0 
percentage correspondance:  1.0

¶

6.2) Prediction on SmartSeq HCC1806

¶

6.2.0) Start by opening the dataset "HCC1806_SmartS_Filtered_Normalised_3000_Data_test_anonim"

In [ ]:
df_SS_HCC1806_anon = pd.read_csv("SmartSeq/HCC1806_SmartS_Filtered_Normalised_3000_Data_test_anonim.txt",delimiter="\ ",engine='python',index_col=0).T
print("Dataframe dimensions:", np.shape(df_SS_HCC1806_anon))

Dataframe dimensions: (45, 3000)

¶

6.2.1) SVM

In [ ]:
predictions_SS_HCC1806_svm = best_svm_SS_HCC1806.predict(df_SS_HCC1806_anon)
In [ ]:
# Convert predictions to a DataFrame and save as CSV
predictions_SS_HCC1806_svm_df = pd.DataFrame(predictions_SS_HCC1806_svm, columns=['Predictions'])
In [ ]:
predictions_SS_HCC1806_svm_df
Out[ ]:
Predictions
0 0
1 0
2 1
3 1
4 1
5 1
6 1
7 0
8 0
9 0
10 0
11 0
12 1
13 1
14 1
15 1
16 0
17 0
18 0
19 0
20 0
21 1
22 1
23 1
24 0
25 0
26 0
27 1
28 0
29 1
30 1
31 0
32 0
33 1
34 1
35 1
36 0
37 0
38 0
39 1
40 1
41 0
42 1
43 0
44 0

¶

6.2.2) CatBoost

In [ ]:
predictions_SS_HCC1806_catboost = best_catboost_SS_HCC1806.predict(df_SS_HCC1806_anon)
In [ ]:
# Convert predictions to a DataFrame and save as CSV
predictions_SS_HCC1806_catboost_df = pd.DataFrame(predictions_SS_HCC1806_catboost, columns=['Predictions'])
In [ ]:
predictions_SS_HCC1806_catboost_df
Out[ ]:
Predictions
0 0
1 0
2 1
3 1
4 1
5 1
6 1
7 0
8 0
9 0
10 0
11 0
12 1
13 1
14 1
15 1
16 0
17 0
18 0
19 0
20 0
21 1
22 1
23 1
24 0
25 0
26 0
27 0
28 0
29 1
30 1
31 0
32 0
33 1
34 1
35 1
36 0
37 0
38 0
39 1
40 1
41 0
42 0
43 1
44 0

¶

6.2.3) Logistic Regression

In [ ]:
predictions_SS_HCC1806_logreg = best_logreg_SS_HCC1806.predict(df_SS_HCC1806_anon)
In [ ]:
predictions_SS_HCC1806_logreg_df = pd.DataFrame(predictions_SS_HCC1806_logreg, columns=['Predictions'])
In [ ]:
predictions_SS_HCC1806_logreg_df
Out[ ]:
Predictions
0 0
1 0
2 1
3 1
4 1
5 1
6 1
7 0
8 0
9 0
10 0
11 0
12 1
13 1
14 1
15 1
16 0
17 0
18 0
19 0
20 0
21 1
22 1
23 1
24 0
25 0
26 0
27 1
28 0
29 1
30 1
31 0
32 0
33 1
34 1
35 1
36 0
37 0
38 0
39 0
40 1
41 0
42 1
43 0
44 0

¶

6.2.4) Final considerations

In [ ]:
k=0
for i in range(45):
    if predictions_SS_HCC1806_svm[i]!=predictions_SS_HCC1806_catboost[i]:
        k=k+1
print("SVM VS CatBoost", "\nnumber errors: ", k, "\npercentage correspondance: ", (45-k)/45)

SVM VS CatBoost 
number errors:  3 
percentage correspondance:  0.9333333333333333
In [ ]:
k=0
for i in range(45):
    if predictions_SS_HCC1806_svm[i]!=predictions_SS_HCC1806_logreg[i]:
        k=k+1
print("SVM VS Logistic", "\nnumber errors: ", k, "\npercentage correspondance: ", (45-k)/45)

SVM VS Logistic 
number errors:  1 
percentage correspondance:  0.9777777777777777
In [ ]:
k=0
for i in range(45):
    if predictions_SS_HCC1806_catboost[i]!=predictions_SS_HCC1806_logreg[i]:
        k=k+1
print("Catboost VS Logistic", "\nnumber errors: ", k, "\npercentage correspondance: ", (45-k)/45)

Catboost VS Logistic 
number errors:  4 
percentage correspondance:  0.9111111111111111
In [ ]:
from scipy import stats


# Stack the predictions to create a 2D array (each row corresponds to the predictions for one instance)
stacked_predictions1 = np.vstack((predictions_SS_HCC1806_svm, predictions_SS_HCC1806_logreg, predictions_SS_HCC1806_catboost))

# Compute the mode along the columns
final_prediction_SS_HCC1806, _ = stats.mode(stacked_predictions1, axis=0)

# Since stats.mode returns a 2D array, we flatten it to get a 1D array
final_prediction_SS_HCC1806 = final_prediction_SS_HCC1806.flatten()

final_prediction_SS_HCC1806
Out[ ]:
array([0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1,
       1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0,
       0], dtype=int64)
In [ ]:
final_prediction_SS_HCC1806_df = pd.DataFrame(final_prediction_SS_HCC1806, columns=['Predictions'])
final_prediction_SS_HCC1806_df.to_csv('final_prediction_SS_HCC1806.csv', index=True)
In [ ]:
k=0
for i in range(45):
    if final_prediction_SS_HCC1806[i]!=predictions_SS_HCC1806_catboost[i]:
        k=k+1
print("Differences final_prediction_SS_HCC1806 and Catboost_SS_HCC1806", k, "\npercentage correspondance final_prediction_SS_HCC1806 and Catboost_SS_HCC1806: ", (45-k)/45)

Catboost VS Logistic 
number errors:  3 
percentage correspondance:  0.9333333333333333
In [ ]:
k=0
for i in range(45):
    if final_prediction_SS_HCC1806[i]!=predictions_SS_HCC1806_svm[i]:
        k=k+1
print("Differences final_prediction_SS_HCC1806 and SVM_SS_HCC1806", k, "\npercentage correspondance final_prediction_SS_HCC1806 and SVM_SS_HCC1806: ", (45-k)/45)

Catboost VS Logistic 
number errors:  3 
percentage correspondance:  0.9333333333333333
In [ ]:
k=0
for i in range(45):
    if final_prediction_SS_HCC1806[i]!=predictions_SS_HCC1806_logreg[i]:
        k=k+1
print("Differences final_prediction_SS_HCC1806 and LogReg_SS_HCC1806", k, "\npercentage correspondance final_prediction_SS_HCC1806 and LogReg_SS_HCC1806: ", (45-k)/45)

Catboost VS Logistic 
number errors:  3 
percentage correspondance:  0.9333333333333333

¶

6.3) Prediction on DropSeq MCF7

¶

6.3.0) Start by opening the dataset "MCF7_Filtered_Normalised_3000_Data_test_anonim"

In [ ]:
df_DS_MCF7_anon = pd.read_csv("DropSeq/MCF7_Filtered_Normalised_3000_Data_test_anonim.txt",delimiter="\ ",engine='python',index_col=0).T
print("Dataframe dimensions:", np.shape(df_DS_MCF7_anon))

Dataframe dimensions: (5406, 3000)

¶

6.3.1) NN

In [ ]:
# Set the model to evaluation mode
model_DS_M.eval()

# Prepare your input data
# Assuming X_new_data is your new dataset in the form of a pandas DataFrame or similar
X_new_tensor_M = torch.tensor(df_DS_MCF7_anon.values, dtype=torch.float32)

# Perform prediction
with torch.no_grad():
    new_output1 = model_DS_M(X_new_tensor_M)

# If the model is a binary classifier, apply sigmoid and round the output
new_predictions1 = torch.round(torch.sigmoid(new_output1))

# If you need to convert predictions to a DataFrame or other format
new_predictions1_df = pd.DataFrame(new_predictions1.numpy(), columns=['Prediction'])

# Print or save the predictions
new_predictions1
# new_predictions_df.to_csv('predictions.csv', index=False)  # Save to CSV if needed
Out[ ]:
tensor([[1.],
        [0.],
        [0.],
        ...,
        [1.],
        [0.],
        [1.]])
In [ ]:
new_predictions_array1 = new_predictions1.numpy()
new_predictions_array_list1=[]
for i in new_predictions_array:
    new_predictions_array_list1.append(i[0])
new_predictions_array_list1
Out[ ]:
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In [ ]:
# Convert predictions to a DataFrame and save as CSV
new_predictions1_df = pd.DataFrame(new_predictions1, columns=['Predictions'])
In [ ]:
new_predictions1_df
Out[ ]:
Predictions
0 1.0
1 0.0
2 0.0
3 1.0
4 1.0
... ...
5401 1.0
5402 0.0
5403 1.0
5404 0.0
5405 1.0

5406 rows × 1 columns

¶

6.3.2) CatBoost

In [ ]:
predictions_DS_MCF7_catboost = best_catboost_DS_MCF7.predict(df_DS_MCF7_anon)
In [ ]:
predictions_DS_MCF7_catboost
Out[ ]:
array([1, 0, 0, ..., 1, 0, 1], dtype=int64)
In [ ]:
# Convert predictions to a DataFrame and save as CSV
predictions_DS_MCF7_catboost_df = pd.DataFrame(predictions_DS_MCF7_catboost, columns=['Predictions'])
In [ ]:
predictions_DS_MCF7_catboost_df
Out[ ]:
Predictions
0 1
1 0
2 0
3 1
4 1
... ...
5401 1
5402 0
5403 1
5404 0
5405 1

5406 rows × 1 columns

¶

6.3.4) Logistic Regression

In [ ]:
predictions_DS_MCF7_logreg = best_logreg_DS_MCF7.predict(df_DS_MCF7_anon)
In [ ]:
predictions_DS_MCF7_logreg
Out[ ]:
array([1, 0, 0, ..., 1, 0, 1])
In [ ]:
# Convert predictions to a DataFrame and save as CSV
predictions_DS_MCF7_logreg_DS = pd.DataFrame(predictions_DS_MCF7_logreg, columns=['Predictions'])
In [ ]:
predictions_DS_MCF7_logreg_DS
Out[ ]:
Predictions
0 1
1 0
2 0
3 1
4 1
... ...
5401 1
5402 0
5403 1
5404 0
5405 1

5406 rows × 1 columns

¶

6.3.5) Final considerations

In [ ]:
k=0
for i in range (5406):
    if predictions_DS_MCF7_catboost[i]!=new_predictions_array1[i]:
        k=k+1
print("Differences CatBoost_DS_MCF7 and SVM_DS_MCF7", k, "\npercentage correspondance CatBoost_DS_MCF7 and SVM_DS_MCF7: ", (5406-k)/5406)

Differences CatBoost_DS_HCC1806 and SVM_DS_HCC1806 95 
percentage correspondance CatBoost_DS_HCC1806 and SVM_DS_HCC1806:  0.9824269330373658
In [ ]:
k=0
for i in range (5406):
    if predictions_DS_MCF7_catboost[i]!=predictions_DS_MCF7_logreg[i]:
        k=k+1
print("Differences CatBoost_DS_MCF7 and LogReg_DS_MCF7", k, "\npercentage correspondance CatBoost_DS_MCF7 and LogReg_DS_MCF7: ", (5406-k)/5406)

Differences CatBoost_DS_HCC1806 and LogReg_DS_MCF7 95 
percentage correspondance CatBoost_DS_HCC1806 and LogReg_DS_MCF7:  0.9824269330373658
In [ ]:
k=0
for i in range (5406):
    if new_predictions_array1[i]!=predictions_DS_MCF7_logreg[i]:
        k=k+1
print("Differences SVM_DS_MCF7 and LogReg_DS_MCF7", k, "\npercentage correspondance SVM_DS_MCF7 and LogReg_DS_MCF7: ", (5406-k)/5406)

Differences SVM_DS_HCC1806 and LogReg_DS_MCF7 58 
percentage correspondance SVM_DS_MCF7 and LogReg_DS_MCF7:  0.9892711801701812
In [ ]:
from scipy import stats


# Stack the predictions to create a 2D array (each row corresponds to the predictions for one instance)
stacked_predictions2 = np.vstack((predictions_DS_MCF7_catboost, new_predictions_array1.T, predictions_DS_MCF7_logreg))

# Compute the mode along the columns
final_prediction_DS_MCF7, _ = stats.mode(stacked_predictions2, axis=0)

# Since stats.mode returns a 2D array, we flatten it to get a 1D array
final_prediction_DS_MCF7 = final_prediction_DS_MCF7.flatten()

final_prediction_DS_MCF7
Out[ ]:
array([1., 0., 0., ..., 1., 0., 1.])
In [ ]:
final_prediction_DS_MCF7_df = pd.DataFrame(final_prediction_DS_MCF7, columns=['Predictions'])
final_prediction_DS_MCF7_df.to_csv('final_prediction_DS_MCF7.csv', index=True)
In [ ]:
k=0
for i in range(5406):
    if final_prediction_DS_MCF7[i]!=predictions_DS_MCF7_catboost[i]:
        k=k+1
print("Differences final_prediction_DS_MCF7 and predictions_DS_MCF7_catboost", k, "\npercentage correspondance final_prediction_DS_MCF7 and predictions_DS_MCF7_catboost: ", (5406-k)/5406)

Differences final_prediction_DS_MCF7 and predictions_DS_MCF7_catboost 66 
percentage correspondance final_prediction_DS_MCF7 and predictions_DS_MCF7_catboost:  0.9877913429522752
In [ ]:
k=0
for i in range(5406):
    if final_prediction_DS_MCF7[i]!=new_predictions_array_list1[i]:
        k=k+1
print("Differences final_prediction_DS_MCF7 and SVM_DS_MCF7", k, "\npercentage correspondance final_prediction_DS_MCF7 and SVM_DS_MCF7: ", (5406-k)/5406)

Differences final_prediction_DS_MCF7 and SVM_DS_MCF7 29 
percentage correspondance final_prediction_DS_MCF7 and SVM_DS_MCF7:  0.9946355900850906
In [ ]:
k=0
for i in range(5406):
    if final_prediction_DS_MCF7[i]!=predictions_DS_MCF7_logreg[i]:
        k=k+1
print("Differences final_prediction_DS_MCF7 and LogReg_DS_MCF7", k, "\npercentage correspondance final_prediction_DS_MCF7 and LogReg_DS_MCF7: ", (5406-k)/5406)

Differences final_prediction_DS_MCF7 and LogReg_DS_MCF7 29 
percentage correspondance final_prediction_DS_MCF7 and LogReg_DS_MCF7:  0.9946355900850906

¶

6.4) Prediction on DropSeq HCC1806

¶

6.4.0) Start by opening the dataset "HCC1806_Filtered_Normalised_3000_Data_test_anonim"

In [ ]:
df_DS_HCC1806_anon = pd.read_csv("DropSeq/HCC1806_Filtered_Normalised_3000_Data_test_anonim.txt",delimiter="\ ",engine='python',index_col=0).T
print("Dataframe dimensions:", np.shape(df_DS_HCC1806_anon))

Dataframe dimensions: (3671, 3000)

¶

6.4.1) NN

In [ ]:
# Set the model to evaluation mode
model_DS_H.eval()

# Prepare your input data
# Assuming X_new_data is your new dataset in the form of a pandas DataFrame or similar
X_new_tensor_H = torch.tensor(df_DS_HCC1806_anon.values, dtype=torch.float32)

# Perform prediction
with torch.no_grad():
    new_output2 = model_DS_H(X_new_tensor_H)

# If the model is a binary classifier, apply sigmoid and round the output
new_predictions_DS_HCC2 = torch.round(torch.sigmoid(new_output2))

# If you need to convert predictions to a DataFrame or other format
new_predictions_DS_HCC2_df = pd.DataFrame(new_predictions_DS_HCC2.numpy(), columns=['Prediction'])

# Print or save the predictions
new_predictions_DS_HCC2
# new_predictions_df.to_csv('predictions.csv', index=False)  # Save to CSV if needed
Out[ ]:
tensor([[0.],
        [0.],
        [0.],
        ...,
        [1.],
        [1.],
        [0.]])
In [ ]:
new_predictions_DS_HCC_array2 = new_predictions_DS_HCC.numpy()
new_predictions_array_DS_HCC_list2=[]
for i in new_predictions_DS_HCC_array2:
    new_predictions_array_DS_HCC_list2.append(i[0])
new_predictions_array_DS_HCC_list2
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In [ ]:
# Convert predictions to a DataFrame and save as CSV
new_predictions_DS_HCC_df = pd.DataFrame(new_predictions_array_DS_HCC_list2, columns=['Predictions'])
In [ ]:
new_predictions_DS_HCC_df
Out[ ]:
Predictions
0 0.0
1 0.0
2 0.0
3 1.0
4 1.0
... ...
3666 0.0
3667 1.0
3668 1.0
3669 1.0
3670 0.0

3671 rows × 1 columns

¶

6.4.2) SVM

In [ ]:
predictions_DS_HCC1806_svm = best_svm_DS_HCC1806.predict(df_DS_HCC1806_anon)
In [ ]:
predictions_DS_HCC1806_svm
Out[ ]:
array([0, 0, 0, ..., 1, 1, 0])
In [ ]:
# Convert predictions to a DataFrame and save as CSV
predictions_DF_HCC1806_svm_df = pd.DataFrame(predictions_DS_HCC1806_svm, columns=['Predictions'])
predictions_DF_HCC1806_svm_df.to_csv('predictions_DF_HCC1806_svm.csv', index=False)
In [ ]:
predictions_DF_HCC1806_svm_df
Out[ ]:
Predictions
0 0
1 0
2 0
3 1
4 1
... ...
3666 0
3667 1
3668 1
3669 1
3670 0

3671 rows × 1 columns

¶

6.4.3) Logistic Regression

In [ ]:
predictions_DS_HCC1806_logreg = best_logreg_DS_HCC1806.predict(df_DS_HCC1806_anon)
In [ ]:
predictions_DS_HCC1806_logreg
Out[ ]:
array([0, 0, 0, ..., 1, 1, 0])
In [ ]:
# Convert predictions to a DataFrame and save as CSV
predictions_DS_HCC1806_logreg_df = pd.DataFrame(predictions_DS_HCC1806_logreg, columns=['Predictions'])
In [ ]:
predictions_DS_HCC1806_logreg_df
Out[ ]:
Predictions
0 0
1 0
2 0
3 1
4 1
... ...
3666 0
3667 1
3668 1
3669 1
3670 0

3671 rows × 1 columns

¶

6.4.4) Final conclusions

In [ ]:
k=0
for i in range (3671):
    if new_predictions_array_DS_HCC_list2[i]!=predictions_DS_HCC1806_svm[i]:
        k=k+1
print("Differences SVM_DS_HCC1806 and Neural_network_DS_HCC1806", k, "\npercentage correspondance SVM_DS_HCC1806 and Neural_network_DS_HCC1806: ", (3671-k)/3671)

Differences SVM_DS_HCC1806 and Neural_network_DS_HCC1806 104 
percentage correspondance SVM_DS_HCC1806 and Neural_network_DS_HCC1806:  0.9716698447289567
In [ ]:
k=0
for i in range (3671):
    if new_predictions_array_DS_HCC_list2[i]!=predictions_DS_HCC1806_logreg[i]:
        k=k+1
print("Differences Neural_network_DS_HCC1806 and LogReg_DS_HCC1806", k, "\npercentage correspondance Neural_network_DS_HCC1806 and LogReg_DS_HCC1806: ", (3671-k)/3671)

Differences Neural_network_DS_HCC1806 and LogReg_DS_HCC1806 96 
percentage correspondance Neural_network_DS_HCC1806 and LogReg_DS_HCC1806:  0.9738490874421138
In [ ]:
k=0
for i in range (3671):
    if predictions_DS_HCC1806_logreg[i]!=predictions_DS_HCC1806_svm[i]:
        k=k+1
print("Differences SVMt_DS_HCC1806 and LogReg_DS_HCC1806", k, "\npercentage correspondance SVM_DS_HCC1806 and LogReg_DS_HCC1806: ", (3671-k)/3671)

Differences SVMt_DS_HCC1806 and LogReg_DS_HCC1806 130 
percentage correspondance SVM_DS_HCC1806 and LogReg_DS_HCC1806:  0.9645873059111959
In [ ]:
from scipy import stats


# Stack the predictions to create a 2D array (each row corresponds to the predictions for one instance)
stacked_predictions3 = np.vstack((new_predictions_array_DS_HCC_list2, predictions_DS_HCC1806_logreg, predictions_DS_HCC1806_svm))

# Compute the mode along the columns
final_prediction_DS_HCC1806, _ = stats.mode(stacked_predictions3, axis=0)

# Since stats.mode returns a 2D array, we flatten it to get a 1D array
final_prediction_DS_HCC1806 = final_prediction_DS_HCC1806.flatten()

final_prediction_DS_HCC1806
Out[ ]:
array([0., 0., 0., ..., 1., 1., 0.])
In [ ]:
final_prediction_DS_HCC1806_df = pd.DataFrame(final_prediction_DS_HCC1806, columns=['Predictions'])
final_prediction_DS_HCC1806_df.to_csv('final_prediction_DS_HCC1806.csv', index=True)
In [ ]:
k=0
for i in range(3671):
    if final_prediction_DS_HCC1806[i]!=new_predictions_array_DS_HCC_list2[i]:
        k=k+1
print("Differences final_prediction_DS_HCC1806 and predictions_DS_HCC1806_Neural_network", k, "\npercentage correspondance final_prediction_DS_HCC1806 and predictions_DS_HCC1806_Neural_network: ", (3671-k)/3671)

Differences final_prediction_DS_HCC1806 and predictions_DS_HCC1806_Neural_network 35 
percentage correspondance final_prediction_DS_HCC1806 and predictions_DS_HCC1806_Neural_network:  0.9904658131299373
In [ ]:
k=0
for i in range(3671):
    if final_prediction_DS_HCC1806[i]!=predictions_DS_HCC1806_logreg[i]:
        k=k+1
print("Differences final_prediction_DS_HCC1806 and LogReg_DS_HCC1806", k, "\npercentage correspondance final_prediction_DS_HCC1806 and LogReg_DS_HCC1806: ", (3671-k)/3671)

Differences final_prediction_DS_HCC1806 and LogReg_DS_HCC1806 61 
percentage correspondance final_prediction_DS_HCC1806 and LogReg_DS_HCC1806:  0.9833832743121765
In [ ]:
k=0
for i in range(3671):
    if final_prediction_DS_HCC1806[i]!=predictions_DS_HCC1806_svm[i]:
        k=k+1
print("Differences final_prediction_DS_HCC1806 and SVM_DS_HCC1806", k, "\npercentage correspondance final_prediction_DS_HCC1806 and SVM_DS_HCC1806: ", (3671-k)/3671)

Differences final_prediction_DS_HCC1806 and SVM_DS_HCC1806 69 
percentage correspondance final_prediction_DS_HCC1806 and SVM_DS_HCC1806:  0.9812040315990194